Network implementation of spectrum analysis

ABSTRACT

Access devices may receive signals over a network and calculate a frequency spectrum of the received signals. An analyzer system may collect the frequency spectrum data from multiple access devices, and based on the collected data, detect, identify, and locate sources of anomalies in a communication network.

CROSS REFERENCE TO RELATED APPLICATIONS

This Application is a Continuation of application Ser. No. 16/594,678filed on Oct. 7, 2019, which is a Continuation of application Ser. No.15/790,422 (U.S. Pat. No. 10,477,422) filed on Oct. 23, 2017, which is aContinuation of application Ser. No. 15/167,349 (U.S. Pat. No.9,826,424) filed on May 27, 2016, which is a Continuation of applicationSer. No. 13/834,962 (U.S. Pat. No. 9,380,475) filed on Mar. 15, 2013.application Ser. No. 13/834,962 claims the benefit of U.S. ProvisionalApplication 61/773,138 filed on Mar. 5, 2013. The entire contents ofthese applications are incorporated herein by reference in theirentirety.

BACKGROUND

Many communication networks include multiple access devicescommunicating with a hub device. Anomalies in a communication channelbetween an access device and the hub may induce signal distortions inthe channel, thereby causing issues such as inter-symbol interference(ISI). A need exists to be able to locate and correct the cause ofdistortions.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the disclosure.

As disclosed herein, the inventors have determined that certain networkfaults have specific characteristics that can be used to identify thetype of fault and to identify the location of the fault. Examples ofsuch anomalies may include micro-reflections due to impedancediscontinuities and the ingress of noise from external sources.

In some aspects, apparatus, systems, and methods are disclosed fordetecting, identifying, and locating the source of anomalies in acommunication network. In various embodiments, access devices maytime-sample communication signals received over the network, and fromthe time-sampled data, calculate frequency characteristics (e.g.,spectrum analysis data) of the network, portions of the network,particular or groups of devices, etc. The frequency characteristics mayinclude in-band or out-of-band characteristics associated with one ormore communication channels in the network and/or includecharacteristics related to status, health, or performance of thenetwork. An analyzer may collect from access devices, for example, dataindicative of spectrum analysis data calculated at each of the accessdevices. In some aspects, the analyzer may then detect and locatevarious anomalies and determine anomaly sources. Such anomalies mayinclude malfunctioning amplifiers, impedance cavities, excessive signalloss/egress, noise ingress, wideband interference/noise, arcing,incorrect plant setup, excessive tilt and leveling, frequency selectiveRF attenuations and notches, excessive attenuation, automatic gaincontrol errors in amplifiers, etc.

Detection may be made by comparing and characterizing the frequency dataover time, across several access devices, and/or over differentfrequency spectrums that include multiple communication channels and/ornon-channel bands. The network topology and frequency response may bedetermined, and with the characterized frequency data, identify andlocate the anomalies.

These and other embodiments are described in more detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 includes a diagram showing elements in an illustrative network inwhich some embodiments may be practiced.

FIGS. 2A-2C include illustrative diagrams of a branch of the network inFIG. 1 in accordance with various embodiments.

FIGS. 3A-3C illustrate frequency spectrum data of a network at an accessdevice according to various embodiments.

FIGS. 4A-4L illustrate user interfaces presenting frequency spectrumdata of access devices that indicate noise caused by different types offaults according to various embodiments.

FIG. 5 illustrates operations in a flow chart that may be performed inaccordance with one or more embodiments.

FIGS. 6A-6B illustrate various data structures in accordance with one ormore embodiments.

FIG. 7 includes the network branch of FIGS. 2A-2C with illustrativeattenuations of a noise source according to various embodiments.

FIGS. 8A-8B illustrate operations in flow charts that may be performedin accordance with one or more embodiments.

FIGS. 9A-9E illustrate various data structures in accordance with one ormore embodiments.

FIG. 10 illustrates a geospatial map in accordance with one or moreembodiments.

FIGS. 11-14 illustrate operations in flow charts that may be performedin accordance with one or more embodiments.

FIG. 15 illustrates a user interface in accordance with one or moreembodiments.

FIG. 16 illustrates an interactive geospatial map user interfaceaccording to one or more embodiments.

FIGS. 17-18 illustrate operations in flow charts that may be performedin accordance with one or more embodiments.

FIG. 19 includes a diagram showing elements of an illustrative computerdevice in which some embodiments may be practiced.

DETAILED DESCRIPTION

FIG. 1 is a block diagram illustrating elements in an exemplary network100 (e.g., access network) according to some embodiments. Each ofmultiple access devices (AD) 101-1 through 101-n communicates with a hub102 across a particular path through network 100. Each of devices 101-1through 101-n may share a portion of its particular communication pathto hub 102 with one or more other access devices 101-1 through 101-n(e.g., access devices on the same street, on the same building floor, orotherwise in a similar geospatial region).

In some embodiments, hub 102 may include a termination system (e.g.,CMTS) or other type of similar system, network 100 may include a network(e.g., optical, hybrid-fiber coaxial (HFC), twisted pair, etc.), andaccess devices 101-1 through 101-n may include modems or other devices(e.g., cable modems, set top terminals, etc.) communicating via thenetwork.

While some embodiments are described in the context of communicationsbetween modems and a termination system in network, other embodimentsmay include different types of access devices (e.g., fiber optic modems,wireless transceivers, gateways, set top terminals, digital videorecorders) and/or different types of hubs (e.g., optical line terminals,wireless base stations, satellites). Such networks may use any ofnumerous communication protocols and various different types of physicalcommunication media (e.g., twisted pair conductors, wireless RFcommunications, fiber optical link, etc.).

In some embodiments, for example, network 100 may be a digitalsubscriber line (DSL) network, hub 102 may be a DSL access module(DSLAM), and access devices 101-1 through 101-n may be DSL modems orother devices communicating via the DSL network. In still otherembodiments, network 100 may be a satellite, cellular, or other wirelessnetwork, access devices 101-1 through 101-n may be transceivers throughwhich users can access the wireless network, and hub 102 may be a basestation or other wireless network hub. In yet other embodiments, network100 may include a Fiber to the Home (FTTH) network, Fiber to thePremises (FTTP) network, passive optical network (PON), RF over glass(RFOG) network, Digital Subscriber Line (DSL) network, multimedia overcoax access (MOCA) network, etc.

In some embodiments, such as ones operated in accordance withData-Over-Cable Service Interface Specification (DOCSIS) standards, acable modem termination system may monitor communications from cablemodems to collect data form access devices. In some embodiments, thenetwork may comprise a hybrid fiber coaxial cable network that carriesvideo data (e.g., a cable television signal) in addition to other data(e.g., packet data in accordance with one or more DOCSIS standards). Forexample, the network may carry data between a data processing facility(e.g., head end) and a set-top-box located in a client premises (e.g., acable television signal) and data between a data processing facility(e.g., head end) and a cable modem located in a client premises (e.g.,packet data in accordance with one or more DOCSIS standards).

Hub 102 may communicate over one or more links 104 (e.g., a GigabitEthernet link) with the Internet, a private IP (internet protocol) datanetwork, and/or other network that allows communications between devices101-1 through 101-n (via hub 102) and one or more external networks. Inthe examples of FIG. 1 and subsequent figures, “n” represents anarbitrary number. Network 100 may include tens, hundreds, thousands ormore access devices, and may be connected to a plurality of othernetworks (e.g., 105, 106). Hub 102 may also utilize links 104 forcommunication with billing servers, network management servers, and/orother network elements. One such network element is analyzer 103.Analyzer 103 may retrieve from hub 102 (or directly from devices 101-1through 101-n via network 100) data that indicates signalcharacteristics in communication paths between access devices 101-1through 101-n, and between the access devices and hub 102. In someembodiments, this data includes in-band and out-of-band (e.g., guardintervals) frequency data of signals received at devices 101-1 through101-n. According to some embodiments, Analyzer 103 may process theretrieved data to characterize devices 101-1 through 101-n, to identifydevices that share communication paths or portions of paths, and todiagnose and locate network problems such as noise/interference ingress,attenuation, malfunctioning network elements, and other anomalies.Although FIG. 1 shows analyzer 103 communicating with hub 102 over link104, analyzer 103 could alternatively be connected to (or be a part of)hub 102, or may alternatively be connected to network 100 itself.

At various times, a noise source (NS) 105 may be present that introducesnoise into the system at one or more access devices (e.g., AD 101-2, AD101-3) and/or at a location within network 100. Noise source 105 mayinclude an external signal or may result from an anomaly that distortssignals present on the network.

FIGS. 2A-2C include illustrative diagrams of a branch of the network ofFIG. 1, in which noise, interference, and/or other anomalies may bedetected according to various embodiments. To providing a non-limitingexample using components relative to a particular network, FIGS. 2A-2Care described with respect to a hybrid coax/fiber network, though othernetworks and components may be used.

Referring to FIG. 2A, at a first end, the network branch may begin at adata processing facility (e.g., head end) that includes a terminationsystem TS (e.g., a cable modem termination system (CMTS)), a modulatorand/or demodulator (e.g. an edge quadrature amplitude modulator anddemodulator), a computing device such as analyzer 103 (not depicted),and combiners equipped to combine multiple signals onto the network. Insome variations, the network may carry optical signals on opticalstrands between the data processing facility (e.g., head end) and anoptical node. The optical node may include an optical combiner/splitter,which receives downstream optical signals at an optical/RF converterthat re-modulates the downstream signals as RF signals onto a coaxialcable network beginning at a communication link (e.g., hardline trunk).The downstream signal may progress over the communication link to anamplifier and then across a feed (e.g., an RG coaxial cable). The feedmay connect to one or more taps that may include communication links(e.g., drop lines) to customer premises and/or network equipment, suchas power supply cabinets (e.g., a unit comprising batteries and atransponder). In an example, the power cabinet may provide back-up powerto a subset of the network active elements (e.g., amplifiers). Thenetwork may continue across additional coaxial cables, amplifiers, andfeeder taps, and filters (not illustrated). The feeder taps may connectto customer premises via a communication link (e.g., drop line) cableand/or a ground block. The signal may enter the customer premisesthrough the ground block, pass through one or more splitters, andconnect to a plurality of customer premises equipment (e.g., a cablemodem, a set top box, etc.). In an example, an upstream signal maytraverse the network in a similar fashion from the customer premisesand/or network equipment to the termination system at the dataprocessing facility (e.g., head end). The optical node may receiveupstream RF signals from the communication link (e.g., hardline trunk)and re-transmit the upstream signals as optical signals.

FIG. 2B illustrates a block diagram of a branch of a network similar toFIG. 2A according to various embodiments. The network branch may beginat a headend or other termination point that may for example include atermination component (e.g. a cable modem termination system (CMTS)),the analyzer 103, and a bidirectional interface (e.g., an opticaltransmitter/receiver). The termination point may be coupled to theremainder of the branch via the bidirectional interface through abidirectional fiber-optic communication path connecting the terminationsystem to a fiber node (e.g., fiber-optic/coax node). The optical nodemay include an optical combiner/splitter, which receives downstreamoptical signals at an optical/RF converter, which drives a modulator,which then transmits RF signals onto the coax network beginning atcommunication link (e.g., hardline trunk) segment S11.

The optical node may also include a de-modulator, which demodulates RFsignals received from the coax network and transmits the demodulatedsignals to an RF/optical converter. The RF/optical converter may thentransmit the converted upstream signals to the combiner/splitter, whichthen transmits the upstream signals to the termination system via theoptical fiber path.

The coaxial branch may include a plurality of communication paths S1-S11interconnected by a plurality of amplifiers A1 and A2, taps T3, T4, andT5, power supply cabinet PS, filters F1 and F2, and combiner/splittersT1 and T2. The network also includes a plurality of access devicesAD1-AD6, such as modems, set-top boxes, transponders, etc. Although notillustrated, groups of access devices located in different facilities(e.g., apartments, condominiums, single-family homes, duplexes, offices,plants, etc.) may be connected through taps and splitter/combiners. Forexample, each facility may include multiple access devices connected toa single tap. While the coaxial branch of the network in FIG. 2Billustrates one topology, other illustrative networks may includeadditional amplifiers, combiners/splitters, taps, and communicationpaths, which may connect hundreds, thousands, or tens of thousands ofaccess devices to the network. Additionally, the network may includeother optical nodes supporting other coaxial branches, which are notshown for convenience.

In an embodiment, each access device AD1-AD6 may time sample the signalsreceived on the network and perform a spectral analysis of thetime-sampled data. For example, access device AD1 may comprise a cablemodem and may perform spectral analysis of a signal received at AD1. Inan example, the spectral analysis may include performing a fast Fouriertransform (FFT) on the received signal that results in datarepresentative of the signal in the frequency domain. In someembodiments, the spectral analysis may output frequency spectrum data inthe form of minimum values, maximum values, average values,instantaneous values, or a combination of these. For example, FIG. 3Aillustrates a frequency spectrum calculated for a signal received at oneAD. In various examples, the time-sampled signals may be over a specificbandwidth. For example, the sampling may be over a single channel (e.g.,6 MHz channel), or over a set of channels (e.g., 1 GHz includingchannels, guard bands, and unallocated out-of-band frequencies).

FIG. 2C illustrates the network branch shown in FIG. 2B, according tovarious embodiments, in which analyzer 103 obtains data regarding signalcharacteristics in each of multiple communication paths within thenetwork. Each path may be associated with an individual access deviceand can represent a physical path from that individual access device tothe fiber node or other signal termination. In some embodiments, one ormore access devices (e.g., cable modems, set-top-boxes, etc.) include aMAC address used to communicate with analyzer 103. As illustrated inFIG. 2C, the data acquired by the analyzer 103 may include frequencyspectrum data of the signals received by each access device connected tothe communication path (e.g., a frequency spectrum of signals receivedat the access device). The analyzer 103 may collect the data bycommunicating with each of the access devices through the communicationpaths. For example, the analyzer 103 may poll each access device fordata. In other examples, each access device may report its data toanalyzer 103 periodically and autonomously. In other aspects, analyzer103 may acquire the data from another device that communicates with theaccess devices to collect the data.

In various examples, a communication branch may include one or moresources of noise or signal distortion. FIGS. 3A-3C and 4A-4L illustratevarious graphical depictions of frequency spectrum data generated fromsignals sampled by an access device. While the data is shown graphicallyin the figures, the data may be stored/represented in other forms, suchas in a database, table, etc.

FIG. 3A illustrates example frequency spectrum data from a single accessdevice in which the data spans a plurality of communications channelsunder nominal conditions (e.g., no anomalies). FIG. 3B illustratesexample frequency spectrum data from multiple access devices in whichthe frequency spectrum data spans a frequency band including a pluralityof communications channels under nominal conditions (e.g., noanomalies). As can be seen, each device may receive the same signals(e.g., exemplified by the spectrum of each device having similarprofiles), but with different amplitudes, which depend upon thedifferent attenuations of the different network paths to each accessdevice. The frequency spectrum may include, for example, a number of 6MHz audio/visual channels (e.g., ATSC channels), a number of 6 MHz datacommunication channels (e.g., DOCSIS), analog video and audio carriers.

FIG. 3C illustrates in more detail a portion of the frequency data ofFIG. 3B for a frequency band including eight 6 MHz channels. In additionto frequency data of each in-band signal (e.g., approximately. 5.4 MHzsignal), the frequency spectrum between the in-band signals (e.g., theout-of-band signals at approximately 0.6 MHz width) may also becaptured.

FIG. 4A illustrates example frequency spectrum data from an accessdevice receiving a signal over the network from an external source(e.g., Long Term Evolution (LTE) wireless standard, 8 VSB transmission,etc.), where the signal has ingressed into a network branch through anetwork fault such as a broken cable shield. Such anomalies may beinduced, for example, by the momentary operation of a motor ortransformer next to an unshielded signal path, or by an externaltransmitter outside of the communication system (e.g., cellular phone,television transmitter, wireless transmitter, etc.). Such anomalies maybe momentary and dynamically change over time.

FIG. 4B illustrates example frequency spectrum data from an accessdevice in the presence of wideband interference, such as power arcing,within the network branch. For example, short between the shield and thesignal wire or between the shield and a power wire may induceinterference over a wide bandwidth (e.g., several channels) with highamplitude that is above the transmitted signals allocated for thatbandwidth. Such interference is indicated by the oval 402 in FIG. 4B.

With respect to FIGS. 4A and 4B, the noise may be characterized from thefrequency spectrum data as originating from a specific type of source(e.g., arcing, LTE transmitter, electric motor, etc.). For example, inFIG. 4A, the oval 401 identifies an ingressing signal that is above theallocated frequency spectrum in the network (e.g., above 700 MHz). Thespecific frequency range of approximately 735 MHz to 745 MHz of theingress signal may be known (e.g., stored in a database) to be withinthe standard frequency band of an LTE channel transmitted from acellular phone in a cellular wireless system. The signal may also bemomentary (e.g., only present when the cellular phone is transmittingand near the unshielded cable.) From these characteristics, the analyzermay determine that the signal is generated from a cellular phonetransmitting in the LTE band near a network fault (e.g., an unshieldedcable). FIGS. 8A and 8B illustrate operations according to variousaspects for detecting and identifying noise source such as those inFIGS. 4A and 4B and for locating the fault at which the noise enters thenetwork.

FIGS. 4C and 4D illustrate example frequency spectrum data from anaccess device that receives signals from a malfunctioning amplifier(e.g., A1). The malfunctioning amplifier may cause frequency selectiveresonant peaking and/or attenuation (e.g., a suck out) within a networkbranch. Under some amplifier failure conditions, network amplifiersexhibit a frequency selective peak 403 or attenuation 404 that can becharacterized by their shapes as highlighted by the ovals in FIGS. 4Cand 4D. In other amplifier failure conditions, network amplifiersexhibit temperature dependent automatic gain control failures as shownin FIGS. 4K and 4L. FIG. 11 includes operational steps of a process fordetecting and locating malfunctioning amplifiers that cause a resonantpeak or attenuation.

FIG. 4E illustrates example frequency spectrum data from multiple accessdevices in the presence of incorrect plant setup such as excessivefrequency tilt and/or leveling, within the network branch. In thenetwork, certain components may exhibit a characteristic or specificfrequency response that is not constant (e.g., flat over the operatingfrequency band). For example, coaxial cable may have a frequencyresponse that exhibits signal attenuation that linearly increases ordecreases with frequency (resulting in decreasing or increasing signalamplitude, respectively, with frequency). Such attenuation, sometimesreferred to as frequency tilt, is illustrated by the frequency data setsshown in FIG. 4E from two respective access devices. One access deviceshows a signal that has linearly increasing amplitude over the frequencyrange of 300 MHz to 731 MHz. A line, L1, illustrates a linearapproximation of the frequency data exhibiting frequency tilt. Whilecertain components, such as coaxial cable exhibit a linear frequencyresponse, other components may exhibit other linear or non-linearfrequency responses.

To adjust for these frequency dependent variations introduced into thenetwork by the network components, the plant may be set up with one ormore correction devices (e.g., filters F1, F2) distributed throughoutthe network to correct for such variations. For example, a filter (e.g.,F2) may be inserted in-line in a network branch, with the filter havinga frequency response that cancels the frequency dependentattenuation/amplifications for signals traversing that branch (e.g., afilter having an inverse response of the frequency tilt of a coaxialcable). By doing so, the frequency response may be leveled as shown bythe frequency data of the second access device shown in FIG. 4E. Line L2illustrates a linear approximation of the frequency data from signalsreceived by the second access device, after the signals have beencorrected by a correction device such a filter. As shown, the slope ofL2 is closer to zero than the uncorrected slope of L1.

The filters or other correction devices may be included at variouspoints in the network, such as at the input of one or more amplifiers,at taps, or in-line between coaxial segments. Frequency data thatexhibits a non-constant response beyond a predetermined threshold (e.g.,having an approximate linear slope beyond a threshold or equal to apredetermined slope within a predetermined margin of error), mayindicate incorrect plant setup. For example, a filter may be needed, ora filter may exist but is failing, or a filter otherwise exhibits aninsufficient response to correct the non-constantattenuation/amplification by the network components. FIG. 12 describedherein provides a process for identifying network components exhibitinga non-constant frequency response that has not been corrected for by acorrection device, and for locating a malfunctioning correction deviceor a place where a correction device is needed to be added.

FIG. 4F illustrates example frequency spectrum data from an accessdevice in the presence of an impedance cavity anomaly that causes astanding wave 405 on the network branch. The impedance cavity may resultfrom the network including multiple impedance mismatches that cause asignal to be reflected back and forth in the network between the twomismatches. A period of the standing wave, T, illustrated in FIG. 4F maybe equal to the time the reflected signal takes to propagate from afirst impedance mismatch to a second impedance mismatch and back to thefirst impedance mismatch. Based on known velocities of propagations ofsignals within the different components (e.g., coaxial cable), adistance between the two mismatches may be determined. FIG. 13 describedbelow provides a process for detecting impedance cavities, detecting thedistance between the impedance mismatches, and locating the impedancemismatch in the network based on the distance and other network data.

FIG. 4G illustrates example frequency spectrum data from multiple accessdevices in the presence of high-end roll-off 406 within the networkbranch. High-end roll-off may be caused by a network failure thatattenuates signals at high frequency. One example could be a wet or dampconductor (e.g., a tap, coaxial cable). For example, water damage at anetwork connection point could inadvertently cause attenuation at highfrequencies as a result of moisture entering a connection point betweentwo components (e.g., between a tap and a coaxial cable).

FIG. 4H illustrates example frequency spectrum data from multiple accessdevices in the presence of passive device (e.g., inductors, capacitors)failures in the network branch. The device failures may result infrequency selective notches 407 (e.g., attenuations). Such notchesappear similar to the attenuations caused by amplifier failures in FIG.4D, but may be distinguished based on shape, with the notches in FIG. 4Hhaving more linear slopes.

FIG. 4I illustrates example frequency spectrum data from multiple accessdevices in the presence of excessive attenuation 408 in the networkbranch. Such attenuation may be caused, for example, by additionalnetwork components, e.g., splitters, being inserted into a network on aclient premise.

FIG. 4J illustrates example frequency spectrum data from an accessdevice in the presence of a band-pass filtering 409. Band pass filtersmay be used in a network to inhibit certain signals from traversingparticular network paths. One example where such filters may be used isselectively providing particular services to a customer, e.g., providingonly data services without providing audio/visual services. A filter mayfor example be installed between a tap and a communication link (e.g.,drop line) to a customer's premise to filter out the audio/visualservices (e.g., television content) to the premise, while permittingdata services (e.g., DOCSIS based network services) to pass through thecommunication link (e.g., drop line). Filters may be installed in someinstances in incorrect locations, causing unintentional filtering ofparticular channels. FIG. 14 described herein provides a process fordetecting, identifying and locating the anomalies shown by the frequencyspectrum data in FIGS. 4G-4J.

FIG. 5 illustrates process 500 that may be performed in accordance withone or more embodiments to identify and/or locate a noise or a noisesource in a network. The process begins at step 510 in which a computingdevice, such as analyzer 103, accesses (e.g., retrieving from a memory,receiving over the network, etc.) and optionally store (for immediate orfuture use) data that characterizes the communication paths between oneor more of access devices AD1 through AD6 and the termination device(e.g., fiber node) at the beginning of the network branch. Such data mayinclude the time-sampled data of signals received over the network ateach access device, or may include frequency spectrum data calculatedbased on the time sampled data.

In some embodiments, step 510 may further include the computing device(e.g., analyzer 103) accessing and/or storing time-sampled and/orspectral analysis data retrieved from the data processing facility,optical node, or other intermediate device within a network branch. Forexample, a spectral analysis (e.g., an FFT) may be performed ontime-sampled data of the downstream signal captured at the dataprocessing facility before the signal traverses the network. Step 510may include multiple iterations of the spectral analysis data beingretrieved and stored, and each iteration may be stored with a timestampand other metadata indicating the source of the data (e.g., dataprocessing facility, AD1, PS, optical node, etc.).

FIGS. 6A-6B shows a portion of a database 150 and 160 generated in step510 by analyzer 103 and stored in a memory (e.g., 1702 described below).For convenience, FIGS. 6A-6B show data in a simple table. The table ofFIGS. 6A-6B is merely one example of how data can be arranged inaccordance with various embodiments. The actual format of data and/or ofthe tables or other data structures used to organize that data will varyamong different embodiments. Each row in table 150 corresponds to aspecific one of the access devices AD1 through ADn. The cells of eachrow contain data related to the corresponding access device and to thecommunication path used by the corresponding access device tocommunicate with the fiber node or other termination device. Cells in afirst column 151 contain index numbers for the rows of table 150. In thepresent example, row 00001 corresponds to device AD1, row 00002corresponds to device AD2, etc. Fields in column 152 contain identifyingdata for an access device on a particular row. In some embodiments, thisidentifying data may include a media access control (MAC) address of theaccess device. Each of columns 153-1 through 153-P represents frequencydata of the signal(s) received from the network by that respectiveaccess device. A cell in a particular row and column contains spectralanalysis data for the access device corresponding to that row and thefrequency corresponding to that column. For each frequency f1-fN, thespectral analysis data may have a real (“r”) and imaginary (“i”)component, with those components represented as “<r>” and “<i>”. Theembodiment displayed in Table 6A illustrates spectral analysis data asreal and imaginary parts of the frequency response. Other embodimentsmay represent similar data in some other manner For example, in someembodiments, the spectral analysis data may be stored as the phase andamplitude values determined (e.g., calculated from an FFT of thereceived signal) from the time sampled signal data. In otherembodiments, only amplitude or phase is stored. At the end of step 510(FIG. 5), each row of table 150 may contain an identifier and spectralanalysis data for up to N frequencies for one or more of the accessdevices AD1 through ADn.

Analyzer 103 may repeat step 510 collecting and storing table 150 formultiple iterations. The iterations may be periodic, occurring at apredetermined rate, or may occur on a varying rate basis (e.g., as fastas data can be collected). Analyzer 103 may store every iteration ofdata, or may store only the most recently collected data (e.g., the mostrecent 2, 3, 4, etc. iterations). During each iteration, analyzer 103may retrieve spectral analysis data for one or more access devices AD1through ADn and generate a time sequence of the retrieved data in step520.

In some embodiments in step 520, the computing device (e.g., analyzer103) stores the time sequence of values in a database 160, such as theone illustrated in FIG. 6B. For convenience, FIG. 6B shows data in asimple table. The table of FIG. 6B is merely one example of how data canbe arranged in accordance with various embodiments. The actual format ofdata and/or of the tables or other data structures used to organize thatdata will vary among different embodiments. In some variations, database150 is a portion of database 160. In each row of database 160, an index161 and access device identifier 162 is included similar to those ofFIG. 6A. Columns 163-1 through 163-T may include a set of data items foreach time iteration. In an embodiment, one data item is a value labeled<t> and may include a start, end, medium, or other time at which theiteration is captured and calculated within a margin of error (e.g.,delta t). In alternative embodiments, each column may include only onevalue <t> associated with all of the values <p> in that column, insteadof storing a separate <t> value for each <p> value. The number ofiterations T may be any value and will depend on the availableresources. In some variations, the columns 163-1 to 163-T may operate asa circular buffer (e.g., FIFO) storing the most recent T iterations. Inan embodiment, the other data item may include a pointer <p> that pointsto a changeable data element depending on a particular use for thespectral analysis data.

In one embodiment, pointer <p> may point to a data table that storesspectral analysis data retrieved from a particular AD in an iteration ata time (e.g., a time <t>), where that data table includes columns 153-1to 153-P from FIG. 6A. As discussed above, columns 153-1 to 153-P storespectral analysis data (e.g., amplitude and phase) with respect tofrequencies f1-fN for a particular AD. In this example, the data tablepointed to by pointer <p> may store the spectral analysis data forfrequencies f1-fN retrieved at time <t> from an AD identified by the ADidentifier (e.g., MAC address) for the row. In some embodiments, pointer<p> may be replaced by one or more actual data values. For example, thedata table illustrated in FIG. 9B includes data values <s> and <f>instead of data item <p>. Data items <s> and <f> will be furtherexplained below with reference to FIG. 9B and a particular example foridentifying/locating ingress noise and/or wideband interference.

At step 530 the computing device (e.g., analyzer 103) may analyze theretrieved spectral analysis data (e.g., amplitude and phase) to identifyan anomaly in the network (e.g., noise ingress, wideband interference,resonant cavity, etc.). In some embodiments, the iterations of retrievedspectral analysis data stored in the data table illustrated by FIG. 6Bmay be analyzed. The analysis may include, for example, identifyingand/or distinguishing between one or more anomalies from amongst aplurality of different types of anomalies (e.g., the anomalies of FIGS.4A-4L) exhibited in the frequency data. This may include performingportions of the processes shown in FIGS. 8A, 8B, and 11-14, to identifydifferent anomalies.

In step 540, a method of analysis may be selected based on the type ofanomaly that is detected, and in step 550, the computing device (e.g.,analyzer 103) may determine the existence and/or location of the anomalyin the network using the analysis selected in step 540. In step 560, theanomaly may be correlated to specific services based on a predeterminedservice allocation database (e.g., a map of video and data services tospecific channels), and based on the impact of the anomaly on particularchannels (e.g., decreasing signal to noise ratio on a channel). In step570, analyzer 103 may determine a course of action to be taken by anetwork operator (e.g., service technician) or by a customer. Suchaction may include for example, repairing or reconfiguring the networkcomponents to correct the anomaly. Another action may be to adapt thesignal transmissions, such as pre-filtering signals before beingtransmitted or reassigning a signal to a different carrier frequency soat to avoid using the frequencies that are adversely effected bythe—anomaly—(e.g., move a carrier away from an LTE transmissionfrequency).

A number of particular variations of the process of FIG. 5 will befurther described below with respect to FIGS. 8A-8B, and 11-14. Theprocesses may be used together, with portions of each process firstidentifying respective types of anomalies as in steps 510-530, and basedon the identified anomalies, deciding which of the processes to continuein step 540 to determine the location of the anomaly as in step 550.

As one example variation, FIG. 7 illustrates a network diagram similarto FIGS. 3A and 3B that includes an ingress noise or widebandinterference source and FIGS. 8A-8B illustrate a method for identifyingand/or locating the noise/interference source in the network.

FIG. 7 depicts the network branch of FIGS. 3A-3B including an example ofnoise ingress such as that depicted in the frequency spectrum data asshown in FIG. 4A or 4B. In FIG. 7, segment S3 may be damaged orotherwise configured to permit the ingress of an external signal intothe network. This could be, for example, a damaged coaxial lineconnecting splitter T1 to splitter T2. The ingress of noise sources mayoccur at different times, and the noise sources may have differentlevels of power and may have different frequency components at the pointof ingress. While the sources are described in the following examples asnoise ingress as illustrated in FIG. 4A, the sources may alternativelybe from a wideband interference source as previously described withrespect to FIG. 4B.

FIG. 7 depicts two illustrative sources of noise ingress in segment S3.A first noise source (the black triangle), may for example have a firstfrequency F1 (e.g., 600-750 MHz) and induce a 20 dBmV level signal ontosegment S3 at the point of ingress. A second noise source (the whitetriangle), may for example have a second frequency F2 (e.g., 5-42 MHz)and induce a 20 dBmV level signal onto segment S3 at the same point ofingress. For ease of explanation, various embodiments are describedherein with respect to one point of ingress as illustrated in FIG. 7. Invarious other embodiments, multiple noise sources through multiplepoints of ingress may be detected.

A noise source may traverse the network from a point of ingress andreach the receiver of an access device or a receiver of another deviceconnected to the network (e.g., a fiber node, test equipment, etc.). Thereceived noise may cause interference with the intended downstream andupstream communications between the access devices and the fibernode/termination system.

In various embodiments, analyzer 103 may acquire spectral analysis data(e.g., a frequency spectrum) from the access devices at differentmoments of time. By analyzing the spectral analysis data, variousembodiments may identify and/or locate noise ingress along one or morepaths in the network. Various examples include the analyzer 103obtaining multiple samples of spectral analysis data from one or moreaccess devices and detecting changes in the spectral analysis data overtime in order to determine the presence and/or location of noiseingress.

As a noise source propagates through the network, the noise will beattenuated, amplified, and/or distorted through line loss and throughnetwork components such as splitters, taps, amplifiers, etc. As such,different access devices having different physical paths to the noisesource will receive varying degrees of interference with the modulatedsignal. Various aspects compare differences between spectral datareceived from the access devices to identify a type of noise sourceand/or to determine a location of noise ingress.

FIG. 7 depicts the network with illustrative attenuations of the twonoise sources F1 (e.g., 600-750 MHz) and F2 (e.g., 5-42 MHz) at variouspoints along the communication path. The attenuation by each componentmay depend on the component function, structure, electrical properties,signal frequency, signal propagation direction, other signal properties,and combinations thereof. For example, a cable (e.g., RG6 coaxial cable)may attenuate a 5-42 MHz signal by approximately 1 dB for every 100 feetof cable length and attenuate a 600-750 MHz signal by approximately 5 dBfor every 100 feet of cable length. A tap may have an approximateinsertion loss of 1 dB and a tap isolation of 20 dB for signals in therange of 5-750 MHz. A splitter may have an approximate 4 dB insertionloss and 20 dB tap isolation for signals in the range of 5-750 MHz. Anamplifier may be configured to amplify signals in both directions, butthe direction of amplification may be frequency selective. For example,in a coaxial system compliant with Data Over Cable Service InterfaceSpecification (DOCSIS) standards, amplifiers A1 and A2 may be designedto block upstream signals in the frequency range of 600-750 MHz, butamplify signals by a gain factor G (e.g., 10 dB) in the 5-42 MHz range.These attenuation and gain values are illustrative only, and othervalues may be applied based on the characteristics of the specificnetwork of the various embodiments.

Based on the example attenuation values above, the levels of F1 having afrequency in the 600-750 MHz range and F2 having a frequency range of5-42 MHz are depicted propagated on different segments of FIG. 7. F1 forexample propagates to amplifier A1 attenuated by 14 dBmV (e.g., −5 dBmVfrom S3A, −4 dBmV from T2, −2 dB from S5, −1 dB from T3, −2 dB from S6).Beyond S6, however, F1 may be blocked by amplifier A1 from propagatingto segment S7, because F1's frequency is outside the upstream operatingfrequency of A1. F2 in contrast may propagate to S7 with a 13 dBmVattenuation plus an amplification of 10 dBmV, the gain of A1 in theupstream direction (e.g., −1 dBmV from S3A, −4 dB from T2, −0.5 dB fromS5, −1 dB from T3, −0.5 dB from S6, and +10 dBmV from A1, −0.5 dB fromS7).

FIG. 8A illustrates process 800 that may be performed in accordance withone or more embodiments to identify and/or locate an ingress noise in anetwork. Process 800 describes one variation (e.g., identifying/locatingingress noise or wideband interference) of process 500 from FIG. 5. Theprocess begins at step 810 in which a computing device, such as analyzer103, obtains and stores data that characterizes the communication pathsbetween one or more of access devices AD1 through AD6 and the fiber node(or other termination device) at the beginning of the network branch.Step 810 includes the retrieval steps described above for step 510 ofFIG. 5. For example, at the end of step 810 (FIG. 8A), each row of atable 150 (e.g., table 150 from FIG. 6A) may contain an identifier andspectral analysis data for up to P frequencies for one of access devicesAD1 through ADn.

As in step 510 of FIG. 5, analyzer 103 may repeat step 810 to collectand store table 150 for multiple iterations. The iterations may beperiodic, occurring at a predetermined rate, or may occur on a varyingrate basis (e.g., as fast as data can be collected). Analyzer 103 maystore every iteration of data, or may store only the most recentlycollected (e.g., the most recent 2, 3, 4, etc. iterations).

Table 150 may store in each row a time (not illustrated) at which theiteration was captured, which may be an absolute time, or may be a timerelative to a prior iteration. In an example, for two differentiterations of collected spectral analysis data at different moments intime, analyzer 103 in step 820 may generate comparison data for eachfrequency (e.g., f1 at time 1 is compared to f1 at time 2) of thereceived signal at each access devices AD1 through ADn. For example, asillustrated in FIG. 9A, columns 174-1 through 174-P include thecomparison value, <d>, for each frequency f1 through fN respectively,for each access device 172. The comparison data may be calculated fromconsecutive iterations, may be calculated from two non-consecutiveiterations, or may be calculated from more than two consecutive ornon-consecutive iterations. In some embodiments, the comparison data maybe derived using complex division values calculated between twoiterations of spectral analysis data (e.g., amplitude and phase).Additional data (not shown) may be included for each row, such as thedifference in time(s) between the iterations on which columns 174-1through 174-P are based. In step 820, a single value for each accessdevice may be calculated from the comparison values (e.g., delta values)of the respective set of frequencies (e.g., frequencies f1-fN) for eachaccess device. The single value may be representative of noise receivedat the access device. Column 175 illustrates the single values, <s>, foreach access device, which may represent a noise reception level at thataccess device. The single value of an access device could be, forexample, the absolute value of the RMS sum of the difference values <d>for that access device.

In certain variations, step 820 may include characterizing frequencycomponents of the noise source based on the frequency values 153-1 to153-P or comparison values 174-1 to 174-P. The frequency data may bestored for each access device as <f> in column 176 of FIG. 9A. In someembodiments, one or more frequency peaks may be detected based on ananalysis of the frequency values 153-1 to 153-P or the comparison values174-1 to 174-P. A center value for each frequency peak may be determinedand these values may be stored in column 176.

Analyzer 103 may repeat step 820 periodically as new data is collectedbased on the iteratively collected data in step 810. Analyzer 103 maystore every iteration of data in 174-1 through 174-P, 175, and/or 176,or may store only the most recently collected (e.g., the most recent 2,3, 4, etc. iterations).

During each iteration, analyzer 103 may retrieve data for one or moreaccess devices AD1 through ADn, generate comparison (e.g., <d>) andsummed (e.g. <s>) values for those access devices, and generate a timesequence of values in step 830. In some embodiments in step 830, thecomputing device (e.g., analyzer 103) may optionally store the timesequence of values in a database 180, such as the one illustrated inFIG. 9B. For convenience, FIG. 9B shows data in a simple table. Thetable of FIG. 9B is merely one example of how data can be arranged inaccordance with various embodiments. The actual format of data and/or ofthe tables or other data structures used to organize that data will varyamong different embodiments. In some variations, database 180 is aportion of database 150. In this embodiment, FIG. 9B may be based on thedata table depicted in FIG. 6B, except the <p> column of FIG. 6B may bereplaced with the <s> column and the <f> column in FIG. 9B. In each rowof database 180, an index 181 and access device identifier 182 isincluded similar to those of FIG. 9A. Columns 183-1 through 183-Tinclude a set of values for each time iteration. One value, labeled <t>includes a start, end, medium, or other time at which the iteration iscaptured and calculated within a margin of error (e.g., delta t). Theother value in each column may include the single value <s>, e.g., noisereception level, and frequency data <f> as calculated in columns 175 and176 of FIG. 9A. In alternative embodiments, each column may include onlyone value <t> for all of the rows in that column, instead of storing aseparate <t> value for each row. The number of iterations T may be anyvalue and will depend on the available resources. In some variations,the columns 183-1 to 183-T may operate as a circular buffer (e.g., FIFO)storing the most recent T iterations.

In step 840 in FIG. 8A, one or more noise reception levels <s> of column175 in FIG. 9A and/or in columns 183-1 to 183-T of FIG. 9B may becompared to a predetermined threshold value. A comparison of a noisereception level <s> above the threshold may indicate the momentaryingress of noise at some point in the network branch as shown in FIG. 7.The predetermined threshold value may be the same or different for eachaccess device, and may be autonomously adapted based on a previouscomparison or previous values of <s> for one or more access devices. Forexample, noise reception levels for one or more access devices on anetwork branch may be averaged over a period of time to determine andaverage value at an access device, and the threshold value for detectinga momentary noise source may be adjusted based on the average value. Incertain variations, for a particular access device, one or morecomparisons of <s> to the threshold value over several iterations may beused to detect the ingress of a momentary noise source (e.g., 3 out of 5positive comparisons).

In response to at least one noise reception level <s> being determinedto be above the threshold, noise ingress or wideband interference isdetermined to exist in step 845, and the process continues to step 850.

In response to the noise reception levels <s> being determined to not beabove the threshold, noise ingress or wideband interference isdetermined not to exist in step 845, and the process loops back to step810. Steps 810-840 may be a specific example of steps 510-530 in FIG. 5and step 845 may be a specific example of step 540, in which theanalysis method selected includes the remainder of the steps of FIGS. 8Aand 8B, which are performed as a specific example of step 550.

In step 850, one or more noise reception levels <s> from respectivemultiple access devices for the same time interval <t> are designatedfor use in detecting the noise ingress location. In some variations,only access devices with noise reception levels <s> above the thresholdare designated for detection of a noise ingress location. In othervariations, access devices with noise reception levels <s> below thethreshold, but near an access device with a noise reception level <s>above the threshold are also included for the analysis. In furthervariations, all access devices on a network branch having at least oneaccess device with a noise reception level <s> above the threshold aredesignated for analysis.

For one or more of the access devices designated in step 850, noiseattenuation as a function of the location of noise ingress in thenetwork branch may be determined in step 860. For example in FIG. 7, foreach physical location along the network branch at which the ingress ofnoise may occur into the network branch, an attenuation factor AF may becalculated for a given access device. The attenuation factor AF may be amultiplier or non-linear formula that indicates the attenuation of thenoise signal when it reaches the access device. As illustrated in FIG.7, for example, a noise source F1 entering between S3A and S3B may beattenuated by 10 dBmV when received by AD2. For a given access device(e.g., ADn), an attenuation factor AFn may be expressed as a function ofphysical location of the noise ingress on the network branch and/or afunction of frequency of the noise source (e.g., AFn[location,frequency]). The measure of noise (e.g., noise reception level <s>), ata particular access device (e.g., n), for a particular noise source(e.g., F1), may be determined by the noise level (e.g., N) at the pointof ingress multiplied (or added in decibels) by the attenuation factorAFn (e.g., <s>=N dBmV+AFn dB). In various embodiments, the noise levelat the point of ingress, N, and noise reception levels <s> may representpower, voltage, or current, and may be a maximum, peak, RMS, or otheraverage value. Further, while AFn is expressed as a multiplicationfactor above, <s> may be determined as a non-linear function of N,position, and frequency.

In step 860, the attenuation factor AFn for the access devices may bestored in a database 190 as illustrated in FIG. 9C. For convenience,FIG. 9C shows data in a simple table. The table of FIG. 9C is merely oneexample of how data can be arranged in accordance with variousembodiments. The actual format of data and/or the tables or other datastructures used to organize that data will vary among differentembodiments. For each access device, a row entry is included thatcontains an index 191 uniquely identifying the entry, an access deviceidentifier 192, and an attenuation factor AFn. One example ofdetermining an attenuation factor for each access device in step 860 isshown in FIG. 8B.

In step 861 of FIG. 8B, interconnection of all of the components in thenetwork branch are identified, mapped, and/or stored in a databasegenerated by analyzer 103 or other computing device (e.g., FIG. 2). Oneexample of such interconnect data is illustrated in the database 200illustrated in FIG. 9D. For convenience, FIG. 9D shows data in a simpletable. The table of FIG. 9D is merely one example of how data can bearranged in accordance with various embodiments. The actual format ofdata and/or of the tables or other data structures used to organize thatdata will vary among different embodiments. In database 200, one or morepoints of interconnection (i.e., nodes) between two components thataffect noise transmission and/or attenuation of noise to an accessdevice is listed as a node in column 201. For each node, the associatedrow in the database 200 includes connection information for one or moredevices connected to the node. The devices are listed in columns acrossthe rows. In column 202-1, the first device for each node is listedalong with a terminal of that device that is connected. For example, inthe Node 1 row, the first device ON represents the optical nodeillustrated in FIG. 7, with the terminal 1 (i.e., terminal connected toS11) of the optical node ON listed as connected to Node 1. Column 202-2lists a second device (if one exists) connected to the node. In the Node1 row, for example, column 202-2 lists the first terminal of branchsegment S11 connected to Node 1. Although not shown, additional columnsmay be included to illustrate additional devices connected to each node.Although illustrated as a table, the interconnection data may berepresented in other forms, such as a schematic or wiring diagram.

From database 200, noise signal paths from one or more locations (e.g.,every location) in the network to an access device may be identifiedand/or mapped in step 862. For example, from the location marked by an Xbetween S3A and S3B in FIG. 7, a signal path can be mapped to AD5 astraversing 100 feet of S3 (e.g., S3B), through T2, through the entirelength of S5, through T3, and to AD5.

In step 863, signaling characteristics for one or more components in thenetwork branch are retrieved from a database 210 that is shown in FIG.9E. For convenience, FIG. 9E shows data in a simple table. The table ofFIG. 9E is merely one example of how data can be arranged in accordancewith various embodiments. The actual format of data and/or of the tablesor other data structures used to organize that data will vary amongdifferent embodiments. In database 210, each row includes electricalcharacterization data for a different component. In the present example,the first row includes data for access device AD1. In column 211, anindex number is included in the cell that uniquely identifies each row,and in column 212, a component identifier associated with the respectivecomponent is included in each cell of the row. The cells in columns213-1 to 213-4 include parameters for each component. While four columnsare shown storing parameters, each row associated with each componentmay have more or less than four parameters. For example, in row 6 accessdevice AD6 is shown as having two parameters, each made up of aparameter name (e.g., sig loss) and an associated value (e.g., 0.5 dB).The first parameter, Type, indicates that AD6 is an access device (e.g.,AD). The second parameter represents the signal loss attributable due tothe path between the access device input and the next identifiedcomponent in the network branch (e.g., T3).

In another example in database 210, row 7 illustrates signalcharacteristics of network branch segment S1 illustrated in FIG. 7. Inthis example, segment S1 includes four different parameters. The firstparameter, Type, indicates that S1 is an RG-6 coaxial cable. The secondand third parameters indicate signal attenuation through S1 as afunction of length and frequency. In row 7 column 213-2, attenuation ofsignals in S1 is given as 1 dBmV per 100 feet of cable for signals inthe 5 to 42 MHz range. In row 7 column 213-3, attenuation of signals inS1 is given as 5 dB per 100 feet of cable for signals in the 0.6 to 0.75GHz range. The given frequency ranges and attenuations are only oneexample, and other embodiments may have other ranges, more or lessranges, and other attenuations. In row 7 column 213-4, the entire lengthof S1 is given as 10 ft. Rows 8-17 illustrate similar parameters forother segments in the network branch.

In rows 18 and 19 of database 210, FIG. 9E illustrates parameters foramplifiers A1 and A2. In these examples, amplification is given for eachamplifier for two different frequency ranges in two differentdirections. Row 18 column 213-2, for example indicates that amplifier A1amplifies signals in the 5 to 42 MHz range by 10 dBmV in the upstreamdirection, but attenuates signals in the same frequency band by 60 dBmVin the downstream direction. Row 18 column 213-3 indicates thatamplifier A1 amplifies signals in the 0.6 to 0.75 GHz range by 10 dBmVin the downstream direction, but attenuates signals in the samefrequency band by 60 dBmV in the upstream direction. In rows 20-24,insertion loss (column 213-2) and tap isolation (column 213-3) areillustrated for taps/splitters T1-T5. The cell entries of table 210 areonly a few examples, and other components and other parameters may bespecified. For example, further effects on signal frequency, such asphase shift, phase-frequency distortion, frequency tilt, etc., caused byeach component may also be indicated as parameters.

Returning to FIG. 8B, in step 864 the signaling characteristicsretrieved in step 863 are associated to the identified signal paths instep 862 to determine the noise attenuation as a function of noiseingress location and frequency. In various examples, step 864 results inthe attenuation factors of FIG. 9C. In various examples, the attenuationfactor AFn, may be represented as a piecewise function or pseudo-code,with different portions of the function/code given for various ranges oflocations along the network branch and for various ranges of signalfrequency. For example, for locations of noise ingress along S3, thefunction of AF5 may be:

If (noise ingress location=S3) AND (frequency=5-42 MHz), thanAF5=−C1−C2−C3−C4−C5;where,

-   -   C1=(length along S3 starting from T2)×1 dBmV/100 ft; (e.g., S3        attenuation loss)    -   C2=4 dBmV; (e.g., insertion loss of T2)    -   C3=100 ft×0.5 dBmV/100 ft; (e.g., S5 attenuation loss)    -   C4=1 dBmV; (e.g., insertion loss of T3)    -   C5=0.5 dB; (e.g., signal loss at AD5).

The terms C1 through C5 included in AF5 may be determined fromconnection information in FIG. 9D and the values of each term may bedetermined from the electrical characteristics in FIG. 9E.

As described above, attenuation (e.g., attenuation factor AFn) may be afunction of noise ingress location and frequency. In variousembodiments, the frequency data <f> in the tables of FIGS. 9A and 9B maybe used. For noise ingress location, various embodiments may uselocation data in different forms. In some examples, location could beexpressed as the component where the ingress of noise occurs (e.g., S3,100 ft from T2). In another example, position could be expressed astotal physical distance from the fiber node where the ingress of noiseoccurs (e.g., 1200 ft). If the network branch includes more than onesub-branch, the sub-branch may also be identified (e.g., 1200 ft, branchS3).

In another example, location may be expressed as a geospatial location(e.g., latitude, longitude), which could then be mapped to a specificlocation within the network branch. In certain embodiments, database 210in FIG. 9E may include additional parameters that describe thegeospatial location (e.g., latitude and longitude) of the variouscomponents, or portions thereof. In another example, geospatial locationinformation may be mapped to the network branch with a visual map. Forexample, FIG. 10 illustrates a geospatial map 1000 of a neighborhoodwith the geospatial locations of the network branch in FIG. 7 shown. InFIG. 10, several components of the network branch are shown in thephysical location in which they exist within the neighborhood. Buildingsand structures, e.g., 1001-1006, are shown which may contain somenetwork branch elements such as access devices. For example, building1006 may include access device AD1. Structure 1002 may include S8 and apower supply cabinet comprising AD4. Map 1000 may include textualinformation, icons, and/or other indicators (not shown), which indicatenetwork branch components in particular structures. For example, aportion of the table in FIG. 9E may be included in map 1000, whichdescribes the interconnection of access device AD1 within structure1006. In various embodiments, map 1000 may take the form of aninteractive interface displayed on a monitor or other display device.When a component illustrated on the map is selected or hovered over witha pointing (e.g., mouse, stylus, finger), for example, information(e.g., information from the tables in FIGS. 9A-9E, longitude, latitude,etc.) may be displayed in the form of a pop-up window or other textualdisplay or provided in the form of auditory feedback.

At the completion of step 864 in FIG. 8B, the process may return to step870 in FIG. 8A. In step 870, the ingress location of a noise source isdetermined based on noise attenuation factors and the noise receptionlevels <s> and/or frequency data <f> for multiple access devices overone or more time iterations. For example, for a given time iteration,for a designated access device, the relationship between the noisereception level <s> and noise ingress level N may be calculated asfollows (in decibels).

-   -   <s>=(N+AFn[location, <f>])=>N=(<s>−AFn[location, <f>])

If multiple designated access devices (e.g., AD1 and AD2) detect thesame noise source N, than the relationships above can be used tocalculate the location of noise ingress. For example, using AD1 and AD5,the following relationships may be established.

-   -   (<s1>−AF1[location, <f1>])=N=(<s5>−AF5[location, <f5>])

Given that the noise reception levels at AD1 (e.g., <s1>) and AD5 (e.g.,<s5>), the frequency data at AD1 (e.g., <f1>) and AD5 (e.g., <f5>) andthe attenuation factor functions at AD1 (e.g., AF1) and AD5 (e.g., AF5)have been determined and may be retrieved from the tables in FIGS.9B-9C, and all terms of attenuation factors are known from the tables inFIGS. 9D-9E, location of the noise ingress may be solved from the aboverelationship. When using two designated access devices, a singlesolution for location may be calculated. For example, formulas for AF1and AF5 for a noise source in the location of segment S3 and in thefrequency range of 5-42 MHz may be as follows:AF1=−(200 ft-loc*1 dB/100 ft)−4 dBmV−0.1 dBmV;=+(loc/100 ft)*1 dB−6.1dB;AF5=−(loc*1 dB/100 ft)−4 dBmV−0.5 dBmV−1 dBmV−0.5 dBmV;=−(loc/100 ft)*1dB−6 dB;

-   -   where (loc=location=distance along S3 from T2).

Given a noise reception level at AD1 of <s1>=14.9 dB, and a noisereception level at AD5 of <s5>=13 dB, then location can be calculated asfollows:14.9 dB−(loc/100 ft)*1 dB+6.1 dB=13 dB+(loc/100 ft)*1 dB+6.0 dB;

loc=location=100 ft from T7 on S2.

In various embodiments, the formula above or other relationships may beused for more than two designated access devices. In such a case,various known algorithms may be used to calculate the best-fit solutionfor a location that satisfies the relationships.

In the various examples above, the frequency data (e.g., <f1> and <f5>)may be the same, since it is generated from the same noise source. Inother embodiments, as previously noted with respect to FIG. 9E, variouscomponents may induce distortions in the frequency. In such cases, thefrequency data at different access devices may be different.Nonetheless, using frequency parameters from the table in FIG. 9E, thedistortions may be accounted for in the formulation of the attenuationfactors.

In certain variations, the determined location of noise ingress may betransmitted to a remote device and/or displayed on an interactive map(e.g., FIG. 10) on a display device that provides a geospatial location(e.g., latitude, longitude) of the point of ingress. For example, one ormore servers (e.g., analyzer 103) may perform the steps of FIGS. 8A and8B, and transmit the location to a technician in the field fortroubleshooting and correcting the problem of noise ingress.

In some embodiments, one or more steps of FIG. 8A may be omitted orreplaced. For example, steps 820 and 830 generate and store a timesequence of access device values (e.g., populate data structures such asthose depicted in FIGS. 9A-9B). Step 840 then tests noise receptionvalues against a predetermined threshold based on the values generatedand stored (e.g., values from data structures depicted in FIGS. 9A and9B). In some embodiments, steps 820-840 may be replaced by a comparisonstep that compares the network characterization data for an accessdevice with predetermined expected spectral analysis data for thataccess device. Spectral analysis data for a particular access device maybe predetermined based on network characterization data measured at adata processing facility and expected attenuation based on the networkstructure. For example, referring to FIG. 2B, an expected spectralanalysis data for AD5 may be predetermined based on the signal measuredat the data processing facility (e.g., downstream signal) and theexpected attenuation of the signal based on the network elements betweenAD5 and the data processing facility (e.g., optical strands, opticalnode, S11, A2, S10, T5, S9, T4, S7, A1, S6, and T3). More generally, thedata processing facility may be configured in such a way that AD5 haspredetermined expected spectral analysis data. Accordingly, the spectralanalysis data measured at AD5 may be compared in step 845 to theexpected predetermined spectral analysis data for AD5. If the differencebetween the measured spectral analysis data at AD5 and the expectedpredetermined spectral analysis data for AD5 is greater than athreshold, the process of FIG. 8 may move to step 850. From step 850,the process may continue as previously described.

In some embodiments, noise ingress may be experienced over an unassignedfrequency range. For example, a signal sent from a data processingfacility may carry information on one or more 6 MHz frequency channels(e.g., assigned frequency range). The information may be carried on aphase and/or amplitude modulated signal in the assigned frequency range.An example of an assigned frequency range can be seen in the plotillustrated in FIG. 4A. An amplitude-modulated signal can be seen in thefrequency range from 664 MHz to 697 MHz. In some embodiments, noiseingress may be measured in a signal in a frequency range that isunassigned (e.g., that is not phase and/or amplitude modulated). Forexample, in FIG. 4A, the amplitude measured above 731 MHz (circled)represents ingress noise 401 over an unassigned frequency range.Depending on the one or more AD's that experience this ingress noise, alocation for the noise may be located. For example, the process of FIGS.8A and 8B may be used to locate the noise.

In some embodiments, noise ingress may be experienced over an assignedfrequency range, but detection of the noise may be limited. For example,in the plot illustrated in FIG. 4A, an amplitude modulated signal can beseen in the frequency range from 664 MHz to 697 MHz. In someembodiments, noise may be experienced in this assigned frequency range,but the noise may be undetectable (e.g., below a threshold) across each6 MHz frequency channel. A guard interval may be placed between each 6MHz channel. For example, in FIG. 4A, an interval is illustrated betweeneach 6 MHz channel where little to no amplitude is measured (e.g.,amplitude below a threshold). In some embodiments, the guard intervalsbetween 6 MHz channels are used to detect noise that is otherwiseundetectable across the 6 MHz channels. For example, if amplitude ismeasured across a guard interval (e.g., amplitude above a threshold) atan AD, it may be determined that the signal received at the AD hasexperienced noise ingress since little to no amplitude (e.g., amplitudebelow a threshold) is expected over the guard interval. Depending on theone or more AD's that experience this ingress noise, a location for thenoise may be located. For example, the process of FIGS. 8A and 8B may beused to locate the noise.

In some embodiments, the noise ingress as described above, may insteadinclude wide band interference and/or power arching. For example, theplot illustrated in FIG. 4B depicts wideband interference over afrequency range. Wideband interference may be differentiated from noiseingress based on an energy level for the noise being above apredetermined threshold. Depending on the one or more AD's thatexperience wideband interference, a location for the noise may belocated. For example, the process of FIGS. 8A and 8B may be used tolocate the noise due to wideband interference in the same manner aslocating an external noise source.

As noted above, attenuation of noise ingress and wideband interferencemay be frequency dependent (e.g., different for different frequencybands). In various examples, wideband interference and noise ingress mayhave bandwidths that span frequencies (e.g., F1 and F2) that havedifferent attenuations throughout the network. In such cases, theanalysis above to locate a noise source may be performed separately forone or more different frequency bands in the noise/interferencebandwidth. In the table in FIG. 9E, for example the network is shown toexhibit different attenuations in two different frequencies bands (e.g.,5-42 MHz and 0.6-0.75 GHz). For noise ingress or wideband interferencespanning both of these frequency bands, the process of FIGS. 8A and 8Bmay be performed on the spectrum data from the access devices over eachof these frequency bands separately, with detection and location of thenoise determined based on just one of the analyzed frequency bands, orbased on the results of more than one band. For example, a location of anoise source may be determined separately for each frequency band,resulting in multiple identified locations of noise ingress. Thedetermined locations may then be compared to determine if the detectednoise is a common noise source, or different noise sources. In oneexample, if the distances (e.g., geographically or linearly along thenetwork path) between the locations is less than a predeterminedthreshold, the noises may be determined to be from a single source. Ifthe distances between the noise locations is above the predeterminedthreshold, the noises may be determined to be from different sources. Ifdetermined to be from a single source, the locations may be combined(e.g., averaged) to determine a more precise location of the noisesource.

FIG. 11 illustrates a process 1100 for identifying and locating amalfunctioning amplifier in a network. The malfunctioning amplifier(e.g., A1 or A2 of FIG. 2B) may induce a frequency peak or attenuation(e.g., a suck out) as previously described with respect to FIGS. 4C and4D, respectively. Process 1100 describes one variation (e.g.,identifying/locating a malfunctioning amplifier) of process 500 fromFIG. 5. The process begins at step 1110 in which a computing device,such as analyzer 103, accesses and, optionally, stores data thatcharacterizes the communication paths between one or more of accessdevices AD1 through AD6 and the fiber node (or other termination device)at the beginning of the network branch. Step 1110 may include theretrieval and storage steps described above for steps 510 and 520 ofFIG. 5. For example, at the end of step 1110, each row of a table 150(e.g., table 150 from FIG. 6A) may contain an identifier and spectralanalysis data for up to P frequencies for one of access devices AD1through ADn.

As in step 510 and 520 of FIG. 5, analyzer 103 may repeat step 1110 tocollect and store table 150 for multiple iterations. The iterations maybe periodic, occurring at a predetermined rate, or may occur on avarying rate basis (e.g., as fast as data can be collected). Analyzer103 may store every iteration of data, or may store only the mostrecently collected (e.g., the most recent 2, 3, 4, etc. iterations). Incertain variations, step 1110 may average or accumulate the collecteddata over time, which may include, for each frequency, accumulatingand/or averaging the data over every iteration from a selected startingpoint in time, or may include a windowed average of a predeterminednumber of the most recent iterations of data. In some examples, only theaccumulated or average values are stored in a memory.

In step 1120, analyzer 103 may retrieve the data stored in 1110 for oneor more access devices AD1 through ADn, and analyze the data forindications of an amplifier malfunction. For example, the collected datafrom step 1110 may, when illustrated as a graph, appear as in FIG. 4Chaving a frequency peak or as in FIG. 4D having an attenuation at aparticular frequency.

Step 1120 may detect a frequency peak in the data for an access deviceby, for example, detecting a frequency band that exceeds a predeterminedamplitude for a predetermined bandwidth as illustrated in FIG. 4C. Thepredetermined amplitude may be for example, an absolute amplitude (e.g.,−20 dBmV), or may be a relative amplitude (e.g., +10 dBmV over theaverage amplitude) of a predetermined transmission band (e.g., 505 MHzto 517 MHz). The bandwidth could be, in various examples, a minimumwidth to distinguish the peak from transit noise. For example, the upperand lower limits of the frequency band having the peak could bespecified as where the amplitude falls within −3 dBmV from the centerfrequency amplitude (or other predefined level). Detecting a frequencypeak may further be based on instantaneous frequency measurements oraverage frequency measurements in which a number of frequencymeasurements are averaged over time. In some examples, a frequency peakmay be detected by curve fitting the frequency data (e.g., to amulti-order polynomial) over a limited bandwidth (e.g., 20 MHz). Forexample, a window of a predefined frequency bandwidth may be swept overthe frequency data (e.g., results calculated for the window positionedat different locations across the full bandwidth) of a particular accessdevice, and at each location of the window, a curve fit of the windoweddata could be performed. The fitted curve could then be compared, withina predetermined margin of error, to predetermined curves (e.g., asignature) characteristic of frequency peaks of known amplifier errors.For example, the difference between the calculated curve and thepredetermined curve could be integrated over the bandwidth of the windowand compared to a threshold value.

Step 1120 may further detect frequency attenuation (e.g., a suck-out) inthe data for an access device by, for example, detecting a frequencyband that is attenuated to a predetermined amplitude for a predeterminedbandwidth as illustrated in FIG. 4D. The predetermined amplitudeattenuation may be, for example, an absolute amplitude (e.g., −42 dBmV)within a bandwidth with an expected higher amplitude (e.g., −29 dBmV),or may be a relative amplitude (e.g., −10 dBmV over the averageamplitude) of a predetermined transmission band (e.g., 386 MHz to 389MHz). The bandwidth could be, in various examples, a minimum width todistinguish the peak from transit noise. For example, the upper andlower limits of the frequency band having the peak could be specified aswhere the amplitude falls within −3 dBmV from the center frequencyamplitude (or other predefined level). Detecting frequency attenuationmay further be based on instantaneous frequency measurements or averagefrequency measurements in which a number of frequency measurements areaveraged over time. In some examples, a frequency attenuation may bedetected by curve fitting the frequency data (e.g., to a multi-orderpolynomial) in the same manner as curve fitting a frequency peak asdescribed above (e.g., comparing the curve fit data to a signature).

Step 1120 may include storing characterization data (e.g., centerfrequency, bandwidth, peak or attenuation, etc.) for the peaks andattenuations identified in the frequency data of the one or more accessdevices.

If an amplifier malfunction is not detected in step 1120, the processmay return to 1110 through decision block 1125. If an amplifiermalfunction is detected, the process may proceed to step 1130 to locatethe malfunctioning amplifier. Steps 1110-1120 may be a specific exampleof steps 510-530 in FIG. 5 and step 1125 may be a specific example ofstep 540, in which the analysis method selected includes the remainderof the steps of FIG. 11, which are performed as a specific example ofstep 550.

In step 1130, the detected frequency peaks and/or attenuations from step1120 in the frequency data of multiple access devices may be compared toidentify peaks and/or attenuations that are common to multiple accessdevices, or unique to one access device. The comparison may done, forexample by comparing the characterization signature data (e.g., centerfrequencies, bandwidths, fitted curves, etc.) of two peaks orattenuations identified in the data of two different access devices, orby comparing the fitted curves.

In step 1140, for an identified peak or attenuation, access devices on acommon network branch are sorted into two different groups: 1) accessdevices with frequency data that include the identified peak orattenuation, and 2) access devices with frequency data that does notinclude the identified peak or attenuation. Step 1140 may be repeatedfor each different peak or attenuation.

For an identified peak or attenuation, step 1150 identifies thedirection of signals on the network in the bandwidth where the peak orattenuation is located. Amplifiers in the network branch may be designedto transmit upstream (e.g., from access devices to a terminating device)and downstream (e.g., from the terminating device to the access devices)at different frequency ranges. For example, a frequency band of 90 MHzto 800 MHz may be allocated to 6 MHz wide broadcast channels (e.g., highdefinition television channels), which would be transmitted from theterminating system to the access devices, and a frequency band of 30 MHzto 89 MHz may be allocated for back channel communications from theaccess devices to the terminating system. In such an example, the peakand attenuation illustrated in FIGS. 4C and 4D, respectively, would bothbe in frequency bandwidth for signals transmitted from the terminatingsystem to the access devices.

In step 1160, one or more amplifiers may be identified in the network ascandidates for generating the peak or attenuation based on theamplifiers' relative position to the group of access devices thatinclude the peak or attenuation, based on the amplifiers' relativeposition to the group of access devices that do not include the peak orattenuation, and/or based on the direction of the signals in thefrequency band of the peak or attenuation.

For example, a candidate amplifier may be identified by determining thatthe amplifier is along the signal path in the network between the groupof amplifiers that includes the peak or attenuation and the group thatdoes not include the peak or attenuation. For example, referring to FIG.2B, if AD4 and AD6 do not include the anomaly, but AD1, AD2, AD3, andAD5 do exhibit the anomaly, amplifier A1 may be determined to be acandidate amplifier that is causing the peak or attenuation.

In another example, a candidate amplifier may be identified bydetermining which amplifiers transmit to at least one of the accessdevices that include the anomaly and based on the direction of signalsin the frequency band where the anomaly is located. For example, if AD6has data that includes a peak in a frequency band where signals aretransmitted from the terminating system to the access devices, amplifierA2 may be determined to be the only possible amplifier that transmitssuch signals to AD6, and thus be included as a candidate amplifier. Step1160 may be repeated for each identified peak or attenuation.

In step 1170, each candidate amplifier may be geospatially located basedon stored data that correlates network components to physical locations.For example, candidate amplifiers may be located on the map in FIG. 10,by latitude and longitude, by street address, etc. The map in FIG. 10may be generated and presented as a user interface. Step 1170 mayinclude outputting the location on a display (e.g., on a displayed mapoutput by analyzer 103 or a display of a remote device). Step 1170 maybe repeated for each identified peak or attenuation.

Process 1100 may also be used to detect other anomalies known to occurat amplifiers, such as automatic gain control error as illustrated inFIGS. 4K and 4L. FIGS. 4K and 4L show frequency data of the same accessdevice, but at two different temperatures, 95 degrees Fahrenheit and 55degrees Fahrenheit, respectively. As shown in the figures, the amplitudeof the signals are higher in FIG. 4L, where the temperature is lower.Such variation may be indicative of faulty automatic gain control in anamplifier.

To detect such an error, step 1120 may compare amplitude (e.g.,integrated over a predefined bandwidth) for an access device at twodifferent temperatures. Temperature data may be acquired for example,based on public weather reports, and the frequency data may be collectedin step 1110 when the temperatures are within predetermined ranges(e.g., above a threshold first temperature and below a threshold secondtemperature that is lower than the first temperature). In step 1140,when the comparison results in a difference that is greater than apredetermined threshold (e.g., stored in a memory), the access devicesmay be grouped into a group designated as exhibiting this particulartemperature dependent fault. Likewise, access devices having acomparison less than the predetermined threshold may be grouped into agroup designated as not exhibiting this particular temperature dependentfault. Once the access devices are grouped, steps 1150 to 1170 proceedas previously described.

FIG. 12 illustrates a process 1200 that may be performed in accordancewith one or more embodiments to identify and/or locate incorrect plantsetup, such as detecting a missing or malfunctioning component (e.g., afilter) that is designed to correct a predetermined non-constantfrequency response (e.g., frequency tilt) introduced by one or morecomponents in the network. The incorrect plant setup may be as describedherein with respect to FIG. 4E.

Process 1200 describes a variation of process 500 from FIG. 5 foridentifying/locating incorrect plant setup. The process begins at step1210 in which a computing device, such as analyzer 103, obtains and,optionally, stores data that characterizes the communication pathsbetween one or more of access devices AD1 through AD6 and the fiber node(or other termination device) at the beginning of the network branch.Step 1210 may include the retrieval and storage steps described hereinfor steps 510 and 520 of FIG. 5. For example, at the end of step 1210,each row of a table 150 (e.g., table 150 from FIG. 6A) may contain anidentifier and spectral analysis data for up to P frequencies for one ofaccess devices AD1 through ADn.

Analyzer 103 may repeat step 1210 to collect and store table 150 formultiple iterations in the same manner as described herein with respectto step 510 of FIG. 5.

In step 1220, analyzer 103 may retrieve the data stored in 1210 for oneor more access devices AD1 through ADn, and analyze the data forindications of incorrect plant setup, such as a missing or malfunctionfilter that would cause the frequency tilt as illustrated in FIG. 4E.For example, the collected data from step 1210 may, when illustrated asa graph, appear as in FIG. 4E having frequency tilt as indicated by lineL1.

Step 1220 may detect frequency tilt or other non-constant frequencyresponses of a network component by, for example, linear approximating,or curve fitting to a polynomial, the frequency data of an accessdevice, and then comparing the approximation/curve fit to predeterminedknown frequency responses (e.g., signatures) of network components. Thecomparison could, in one example include comparing (within apredetermined margin of error) the slope of a linear approximation ofthe frequency data to a known slope (e.g., tilt) introduced by aspecific type of coaxial cable (e.g., RG6) within a particular frequencyband. In other examples, the comparison could include an integrateddifference, a cross-correlation, etc., between the approximated curveand the known curve (e.g., a signature) associated with particularcomponents in the network. If the comparison indicates a match to aparticular network component (e.g., the integrated difference beingbelow a threshold value, the cross-correlation being above a thresholdvalue) the type of component and the access device at which the matchwas detected may be stored as an associated pair of data. Step 1220 maybe repeated for multiple access devices in the network.

If a component malfunction or incorrect plant setup is not detected instep 1220, the process may return to 1210 through decision block 1225.If an amplifier malfunction is detected, the process may proceed throughblock 1225 to step 1230 to locate the component malfunction or incorrectplant setup location. Steps 1210-1220 may be a specific example of steps510-530 in FIG. 5 and step 1225 may be a specific example of step 540,in which the analysis method selected includes the remainder of thesteps of FIG. 12, which are performed as a specific example of step 550.

The detected component/access device data pair from step 1220 ofmultiple access devices may be compared in step 1230 to identify accessdevices having frequency data indicative of the same network componentshaving the non-constant frequency response (e.g., tilt).

In step 1240, access devices on a common network branch are sorted intotwo different groups: 1) access devices with frequency data thatincludes the non-constant frequency response of a particular component(e.g., tilt from a coaxial cable), and 2) access devices with frequencydata that do not include the non-constant frequency response of theidentified component. Step 1240 may be repeated for each differentidentified component.

For each identified component, step 1250 may identify the direction ofsignals on the network in the bandwidth where the non-constant frequencyresponse was identified. For example, the frequency tilt detected inFIG. 4E is in the bandwidth from 125 MHz to 731 MHz, which may beallocated for downstream transmissions (e.g., from the terminationsystem to the access devices).

In step 1260, components having a characteristic frequency response thatmatches the detected non-constant frequency response are identified aspossible sources of the anomaly. Of the possible source components,those in the signal paths (based on the determined signal direction) ofthe access devices in the group having the frequency response, but notin the signal paths of the access devices in the group not having thefrequency response are identified as candidate components that generatethe non-constant frequency response.

For example, in FIG. 2B, devices AD1 and AD2 may exhibit the frequencytilt corresponding to L1 in FIG. 4E for downstream signals, and deicesAD3-AD6 may exhibit relatively lower tilt as indicated by L2 in FIG. 4E.The tilt of L1 may be determined to correspond to coaxial cable segmentsS1-S10 as possible sources. Of S1-S10, only S1-S3 are determined to bein the downstream signal path of AD1 and AD2, which exhibit the tilt,and not in the signal paths of AD3-AD6, which do not exhibit the tilt.Based on the determination, S1-S3 are identified as candidatecomponents. Step 1260 may be repeated for each identified non-constantfrequency response known to correspond to a network component.

In step 1270, for each candidate component, candidate correction devicesand their locations in the network are identified for correcting thenon-constant frequency response. The candidate correction devices (e.g.,filters) could be already present, but not tuned or operating correctly,or could be missing and required to be added. Already present correctioncomponents, in step 1280, may be geospatially located based on storeddata that correlates network components to physical locations. Forexample, candidate filters may be located on the map in FIG. 10, bylatitude and longitude, by street address, etc. Step 1280 may includeoutputting the location on a display (e.g., on a displayed map output byanalyzer 103 or other remote device). Similarly, for candidatecorrection devices that do not exist, appropriate locations (networkpath or geospatial) for correcting the non-constant response may beidentified and/or displayed. Step 1280 may be repeated for eachdifferent candidate component. Based on the identified locations,already present or new correction devices may be tested within thenetwork at the identified locations.

FIG. 13 illustrates a process 1300 that may be performed in accordancewith one or more embodiments to identify resonant cavities within thenetwork and to locate a network fault that is causing the resonantcavity (e.g., an impedance mismatch). The resonant cavity may be asdescribed herein with respect to FIG. 4F. Process 1300 describes avariation of process 500 from FIG. 5 applied to resonant cavities.

The process begins at step 1310 in which a computing device, such asanalyzer 103, obtains and, optionally, stores data that characterizesthe communication paths between one or more of access devices AD1through AD6 and the fiber node (or other termination device) at thebeginning of the network branch. Step 1310 may include the retrieval andstorage steps described herein for step 510 of FIG. 5, which may resultin the data in table 150 illustrated in FIG. 6A. Analyzer 103 may repeatstep 1310 to collect and store table 150 for multiple iterations in thesame manner as described herein with respect to step 510 of FIG. 5.

In step 1320, analyzer 103 may retrieve the data stored in step 1310 forone or more access devices AD1 through ADn, and analyze the data forindications of a standing wave caused by an impedance cavity. Forexample, the collected data from step 1310 may, when illustrated as agraph, appear as in FIG. 4F with a periodic standing wave across thefrequency spectrum (e.g., a periodic increase and decrease in signalamplitude across frequency).

Step 1320 may detect a standing wave by, for example, detecting localminimum or maximum amplitudes at multiple frequencies in the frequencydata of an AD. For example, local minimum amplitudes may be found byscanning the data across frequency bands, and detecting frequency bandswhere amplitudes at adjacent frequencies above and below the frequencyband have greater values than the amplitude at the frequency band beingevaluated. To avoid detecting spurious minimums and maximums, thefrequency data may be filtered to remove frequency components in thedata that are above or below an expected or designated frequency atwhich the standing wave is to be detected. Local maximum amplitudes maybe found in a similar way by detecting frequency bands where amplitudesat adjacent frequencies above and below the frequency band have lowervalues than the amplitude at the frequency band being evaluated. Oncelocal minimum or maximum amplitudes are detected, a standing wave isdetected by measuring periodicity of the local maximum or minimumamplitude to within a threshold tolerance.

In other examples, a standing wave may be detected by performing aFourier Transform (e.g., a Fast Fourier Transform (FFT)) on thefrequency data. Standing waves will be shown by a peak in the FourierTransform, with the amplitude and time of the peak being respectivelyrepresentative of the amplitude and time period of the standing wave.Step 1320 may be repeated for multiple access devices in the network.

If a standing wave is not detected in step 1320, the process may returnto 1310 through decision block 1325. If a standing wave is detected, theprocess may proceed through block 1325 to step 1330 to locate the faultcausing the standing wave. Steps 1310-1320 may be a specific example ofsteps 510-530 in FIG. 5 and step 1325 may be a specific example of step540, in which the analysis method selected includes the remainder of thesteps of FIG. 13, which are performed as a specific example of step 550.

In step 1330, the detected standing waves from step 1320 of multipleaccess devices may be compared to identify access devices havingfrequency data indicative of the same impedance cavity, e.g., having thesame periodicity and/or amplitude.

In step 1340, access devices on a common network branch are sorted intotwo different groups: 1) access devices with frequency data that includethe detected standing wave, and 2) access devices with frequency datathat does not include the detected standing wave. Step 1340 may berepeated for each different standing wave (e.g., different period T1).

For each identified standing wave, step 1350 evaluates the topology ofthe network to identify candidate portions of the network on which thefault(s) may exist, based on one or more factors, including the groupsof access devices that do/do not exhibit the standing wave, and based onthe transmission and isolation properties of the network components forsignals in the frequency range in which the standing wave is detected(e.g., tap isolation, amplifier directionality, etc.). Step 1350 mayinclude identifying each network segment (e.g., S1, S2, and S3) thatconnects access devices in the group of access devices that exhibit aparticular standing wave, and identifying each network segment thatconnects access devices in the group of access devices that do notexhibit the standing wave. For example, referring to FIG. 2B, assumingthe frequency data from AD1-AD3 and AD5 exhibited a standing wave havingthe same period, and the frequency data from AD4 and AD6 did not exhibitthe standing wave, segments S1-S6 would be identified as possiblyincluding the faults causing the standing wave, and segments S7-S11would be excluded from those segments possibly including the fault.

Step 1350 may further include identifying network components (e.g.,taps, amplifiers, filters), that would prevent the standing wave frompropagating from one segment to another in the frequency range in whichthe standing wave is detected. For example, referring to FIG. 4F, thestanding wave is shown in the range of approximately 100 MHz to 460 MHz.According to the component data in table 9E, for example, the amplifiersA1 and A2 exhibit high attenuation (−60 dB) in the upstream directionfor signals in that frequency range, and thus, any standing wave signalwould not propagate past an amplifier in the upstream direction. Asanother example, splitters and taps T1-T5 exhibit 20 dB of tap isolation(e.g., −20 attenuation from tap to tap), which may effectively prevent astanding wave signal from propagating from one tap to another tap.Continuing with the example above, amplifier A1 would prevent thestanding wave from propagating from the group of segments S1-S6 to thesegments S7-S10, This would confirm that the fault is within segmentsS1-S6. In other examples, where both faults between which the standingwave reflects lie between two amplifiers, the standing wave may beprevented from propagating in one direction (e.g., past the upstream,amplifier), but may propagate in the opposite direction (e.g. past thedownstream amplifier).

In the example above, the segments on which the faults exist may furtherbe narrowed based on the tap to tap isolation of T2, which wouldeffectively prevent a standing wave generated on S4 to propagate toS1-S3, and likewise prevent a standing wave generated on S1-S3 frompropagating to S4. Because in the example above, the frequency data ofAD1, AD2, and AD3 exhibit the standing wave equally, the faults thatgenerate the standing wave may be located within S6 and S5. These arethe only segments from which the standing wave would propagate tosegments S3 and S4 equally.

For each identified standing wave, step 1360 includes calculating adistance between the faults creating the standing wave based on theperiod of the standing wave, and based on the velocity of propagation ofthe signals on the segments of the network identified in step 1350. Aspreviously indicated, the period T of a standing wave is representativeof the time a signal takes to propagate from a first impedance mismatchto a second impedance mismatch and reflect back to the first impedancemismatch. Electromagnetic waves travel in free space at a known rate of983,571,056 feet per second (ft/sec), but in a different medium, thewaves propagate only at a faction of the free space velocity ofpropagation. A coaxial cable may carry RF signals, for example, at 87%of the velocity of propagation in free space. As another example, asingle mode optical fiber carrying a light pulse at 1310 nm wavelengthmay have a characteristic velocity of propagation of 68% of the freespace velocity of propagation.

For each of the possible propagation paths identified in step 1350, avelocity of propagation is determined in step 1360. The velocity ofpropagation will depend on the components in the network through whichthe standing wave propagates. Values for a velocity of propagation fordifferent components may be stored as predetermined values in a memory.For example, the component parameters illustrated in FIG. 9E may includeadditional velocity of propagation values stored for particularcomponents (e.g., RG6 and RG11 cables). Based on a velocity ofpropagation of the possible paths of the standing wave signal asdetermined in step 1350 (e.g., S5 and S6), the distance between thefaults may be determined by multiplying the velocity of propagationalong the signal path by the standing wave period T to determine a roundtrip reflected signal distance, which may be divided by two to determinethe distance between faults.

In step 1370, candidate locations of faults creating the standing waveare determined based on the candidate network segments that may includethe standing wave, and based on the calculated distance between faults(e.g., impedance mismatches). In one example, one fault from which thestanding wave is reflected is assumed to be a component in the network,such as an output of an amplifier. A location may then be identified asa fault location based on the calculated distance from the assumedcomponent having the impedance mismatch.

For example, in FIG. 4F, the period T is shown to be approximately 41.7nS ( 1/24 MHz). Assuming a velocity of propagation in segments S5 and S6of 0.87 times the free space velocity of propagation, the distancebetween faults can be determined to be approximately 0.87*983,571,056ft/sec,*41.7 nS/2=17.8 ft. In this example, the fault location would beidentified as 17.8 ft from the location of amplifier 1 along S6. Whilein this example, the amplifier A1 was assumed to be the location of onefault, other devices may be assumed to be a fault location, e.g., taps,splitters, etc.

In some embodiments, more than one standing wave may be detected for aset of access devices. For example, performing an FFT on frequency datafrom an access device may exhibit two peaks, indicating two standingwaves. In such a case, signals may be reflected in a first impedancecavity between a fault and an impedance mismatch at a first device(e.g., amplifier A1), and a second impedance cavity may be formedbetween the same fault and a second device (e.g., tap T3). In such acase, respective distances may be calculated using the process 1300 foreach cavity. In variations where the distances add to the length betweentwo components (e.g., amplifier A1 and tap T3), it can be determinedthat the fault lies between the two components at a first calculateddistance from the first component and a second calculated distance fromthe second component. Step 1370 may be repeated for each differentstanding wave detected.

Step 1380 may include outputting the location(s) to a memory or on adisplay (e.g., on a displayed map output of FIG. 10 by analyzer 103 orother remote device). Step 1380 may be repeated for each different faultlocation.

FIG. 14 illustrates a process 1400 that may be performed in accordancewith one or more embodiments to identify various other anomalies, suchas signal roll off as illustrated in FIG. 4G, frequency notches asillustrated in FIG. 4H, excessive attenuation as illustrated in FIG. 4I,and incorrectly inserted band pass filters as illustrated in FIG. 4J.Process 1400 describes a variation of process 500 from FIG. 5 as appliedto the above faults.

The process begins at step 1410 in which a computing device, such asanalyzer 103, obtains and, optionally, stores data that characterizesthe communication paths between one or more of access devices AD1through AD6 and the fiber node (or other termination device) at thebeginning of the network branch. Step 1410 may include the retrievalsteps described herein for steps 510 and 520 of FIG. 5, which may resultin the data in table 150 illustrated in FIG. 6A. Analyzer 103 may repeatstep 1410 to collect and store table 150 for multiple iterations in thesame manner as described herein with respect to step 510 of FIG. 5.

In step 1420, analyzer 103 may retrieve the data stored in 1410 for oneor more access devices AD1 through ADn, and analyze the data forindications of signal roll off, frequency notches, excessiveattenuation, and band pass filters. For example, the collected data maybe curve fit to polynomials indicative of each of the faults above.

If no faults are detected in step 1420, the process may return to 1410through decision block 1425. If one of the faults is detected, theprocess may proceed through step 1425 to step 1430 to locate the fault.Steps 1410-1420 may be a specific example of steps 510-530 in FIG. 5 andstep 1425 may be a specific example of step 540, in which the analysismethod selected includes the remainder of the steps of FIG. 14, whichare performed as a specific example of step 550.

In step 1430, the detected faults from step 1420 of multiple accessdevices may be compared to identify access devices having frequency dataindicative of the same faults.

In step 1440, access devices on a common network branch are sorted intotwo different groups: 1) access devices with frequency data thatincludes the same fault, and 2) access devices with frequency data thatdoes not include the same fault. Step 1440 may be repeated for eachdifferent fault.

For each identified fault, step 1450 evaluates the topology of thenetwork to identify candidate portions of the network on which thefault(s) may exist. Identifying the candidate network portions may bebased on one or more factors, including the groups of access devicesthat do/do not exhibit the fault, and based on the transmission andisolation properties of the network components for signals in thefrequency range in which the fault is detected (e.g., tap isolation,amplifier directionality, etc.). Step 1450 may include identifying eachnetwork segment (e.g., S1, S2, and S3) that connects access devices inthe group of access devices that exhibits a particular fault, andidentifying each network segment that connects access devices in thegroup of access devices that do not exhibit the fault. For example,referring to FIG. 2B, assuming the frequency data from AD1-AD3 and AD5exhibited the same fault, and the frequency data from AD4 and AD6 didnot exhibit the fault, segments S1-S6 would be identified as possiblyincluding the fault, and segments S7-S11 would be excluded from thosesegments possibly including the fault.

Step 1450 may further include identifying network components (e.g.,taps, amplifiers, filters), that would prevent the fault frompropagating from one segment to another in the frequency range in whichthe fault is detected. For example, referring to FIGS. 4G, 4H, 4I, and4J the faults are shown in the range of approximately 100 MHz to 460MHz. According to the component data in the table in FIG. 9E, forexample, the amplifiers A1 and A2 exhibit high attenuation (−60 dB) inthe upstream direction for signals in that frequency range, and thus,any fault signal would not propagate past an amplifier in the upstreamdirection. As another example, splitters and taps T1-T5 exhibit 20 db oftap isolation (e.g., −20 attenuation from tap to tap), which mayeffectively prevent a fault signal from propagating from one tap toanother tap. Continuing with the example above, amplifier A1 wouldprevent the fault from propagating from the group of segments S1-S6 tothe segments S7-S10, and thus indicate that the fault is within segmentsS1-S6. In other examples, where the fault is between two amplifiers, thefault may be prevented from propagating in one direction (e.g., past theupstream, amplifier), but may propagate in the opposite direction (e.g.past the downstream amplifier).

In the example above, the segments on which the faults exist may furtherbe narrowed based on the tap to tap isolation of T2, which wouldeffectively prevent a fault generated on S4 to propagate to S1-S3, andlikewise prevent a fault generated on S1-S3 from propagating to S4.Because in the example above, the frequency data of AD1, AD2, and AD3exhibit the fault equally, the components that generate the fault may belocated within S6 and S5. These are the only segments from which thefault would propagate to segments S3 and S4 equally.

Step 1460 may include outputting the components on which the fault mayexist to a memory or on a display (e.g., on a displayed map output byanalyzer 103). Step 1460 may be repeated for each different faultlocation. In some embodiments, a display that represents the spectralanalysis data for one or more ADs may be generated. For example,analyzer 103 may generate such a display. FIG. 3A illustrates an exampledisplay representing spectral analysis data received from a single AD.The plot illustrated in FIG. 3A shows an amplitude (measured in dB onthe y-axis) of a signal received at the AD at various frequencies(measured in MHz on the x-axis). The plot may be generated based on asingle iteration of frequency spectrum data received from an AD or maybe based on an average of a plurality of iterations of spectral analysisdata received from the AD.

In some embodiments, a display that represents spectral analysis datareceived from a plurality of ADs may be generated. For example, FIG. 3Billustrates a plot similar to FIG. 3A where the plot shows the amplitude(y-axis) of signals received at a plurality of ADs at variousfrequencies (x-axis). In an example, the plot may be color coded suchthat each AD has a particular color that indicates the depicted spectralanalysis data in that color represents the signal received at thatparticular AD. In some embodiments, the display includes a zoom featurethat enables a portion of the plot to be zoomed. For example, FIG. 3Cillustrates a plot of spectral analysis data received from a pluralityof ADs zoomed over a frequency range between 320 MHz and 366 MHz.

In some embodiments, the frequency spectrum data from one or more ADsdisplayed, for instance, in a plot, may be selected based on one or moreparameters. For example, the ADs may be selected based on a geographicproximity (e.g., located on the same street, within a predeterminedradius of a geographical location, within predetermined geographicalboundaries, etc.). The ADs may also be selected based on their locationon a network. For example, AD1-AD6 may be selected based on one or moreof the ADs sharing a common network path from a data processingfacility, the AD's sharing a common optical node, the ADs experiencing acommon signal distortion, a combination of these, or any other suitablenetwork architecture commonality.

As an example, FIG. 15 illustrates a graphical user interface displaywhere one or more ADs may be selected and subsequently displayed on aplot, such as a plot similar to that illustrated in FIG. 3B. The ADs maybe selected using a dialog box such as box 1501. The dialog box may bepopulated with ADs by performing a search based on one or more of ageographic location, a network path, a combination of these, or anyother suitable parameter, as described above. The ADs may then beselected for display from dialog box 1501. Which ADs are displayed inthe dialog box may be based on the ADs connection to a common node(e.g., an optical node), a MAC address for the AD, a location (e.g.,street address) for the AD, or other suitable criteria.

In some embodiments, the analyzer may identify one or more signaldistortions experienced at one or more ADs based on the displayedspectral analysis data. For example, an analyzer may generate a displaysuch as the plots illustrated in FIGS. 4A-4L which exhibit one or moresignal distortions (e.g., noise ingress, wideband interference, resonantpeaking, RF suckout, tilt, high-end roll-off, a standing wave, a notch,attenuation beyond a threshold, weather related distortion, a band-passfilter, etc.) based on the display. In some embodiments, one or more ADsmay be selected autonomously for display based on the ADs experiencing acommon signal distortion (e.g., a notch), which is detected in thefrequency data of each of the displayed access devices. For example, thesteps of 1120, 1220, 1320, and 1420 may each identify access deviceshaving frequency data that exhibits a fault, and the user interface mayautomatically display the frequency data for just those access deviceson the user interface. In other aspects, the analyzer may automaticallyidentify (e.g., highlight, label, draw a box, etc.) the specific networkcharacteristic (e.g., tilt, peaking, etc.) that is detected.

In some embodiments, the ADs may also be displayed on a geographic map.For example, FIG. 16 illustrates a display where a plurality of selectedADs is displayed on a geographic map. The map may be a street map, asillustrated, or any other geographical map. For instance, a street mapmay be displayed and the selected ADs may be displayed as an overlayover the street map based on a location (e.g., street address)associated with the ADs. In some embodiments, a user may locate one ormore signal distortions experienced at the one or more ADs based on thedisplayed geographical map. For instance, a geospatial area may bedelineated as containing a fault, based on a group of access devices onthe map being determined to have frequency data exhibiting the fault.

FIG. 17 illustrates a process 1700 that may be performed in accordancewith one or more embodiments to generate the frequency spectrum datathat is retrieved in for example, steps 510, 810, 1110, 1210, 1310, and1410. Process 1700 may be performed by an access device entirely or withthe analyzer 103. In step 1710, access device may configure a tuner tocapture frequency data over a predetermined window of time, and in step1720, the captured data is processed (e.g., with an FFT) to generate afrequency spectrum of the captured data.

In some aspects, the tuner is a wideband tuner that samples the networkat a high rate (e.g., the Nyquist rate) sufficient to capture afrequency band that includes several channels. For example, the tunermay sample at the Nyquist rate for an entire allocated bandwidth of thenetwork (e.g., 0-750 MHz). Processing of this data in step 1720 resultsin a full spectrum as shown, for example in FIGS. 4A-4L that can be usedin the processes described herein to detect faults.

In other aspects, the tuner is capable of only tuning to a singlechannel (e.g., a 6 MHz bandwidth), which is down-converted and then timesampled. In such a case, only a limited window of frequency data aboutthe center frequency of the channel may be calculated. For example, insome variations, only the signal-to-noise ratio (SNR) of the channel maybe determined by the access device. The SNR of a single channel may betreated as a single 6 MHz wide frequency bin. The tuner may then betuned to multiple different channels, with the SNR retrieved for eachchannel. The SNRs may then be ordered sequentially by frequency torepresent a low-resolution frequency spectrum data that can be used inthe processes disclosed herein for detecting and locating faults.

In other variations, pre-equalization coefficients of an access devicemay be used to derive the in-channel frequency response (ICFR) of thenetwork over a single channel. Various access devices, for example, willinclude a pre-equalizer and/or post equalizer that will pre and postequalize signals transmitted from and received at the access device,respectively. The equalization coefficients of the equalizers may beadaptive and set in response to the frequency response of the channel towhich the tuner is tuned. That is, the equalizers are configured tocancel out distortions induced by the network. By taking the inverse ofthe equalizer coefficients, the in-channel frequency response of thechannel is obtained. The tuner can be tuned to multiple channels toobtain the in-channel frequency response of multiple channels.

In step 1730, the frequency data from the multiple different methods ofcapture for an access device may be combined to provide a higherresolution spectrum. For example, the in-channel frequency response foreach channel can be combined with other frequency data to provide ahigher resolution spectrum. For example, the in-channel frequencyresponse of a particular channel can be overlayed/combined with the samefrequency band of data obtained in the full spectrum capture to providehigher resolution information within that band. For example, if the fullspectrum frequency data exhibits a standing wave, and a minimum of thestanding wave falls within a channel, in-channel frequency response ofthat channel may be overlayed with the frequency data of the fullspectrum data within the channel bandwidth to provide a higherresolution image of that bandwidth. Likewise, the SNR data of eachchannel, when viewed in frequency sequential order, may show a courserepresentation of a standing wave. The in-channel frequency response ofeach channel may be normalized to the SNR of that channel and sequencedtogether to provide a higher resolution picture of the standing wave.

In step 1740, the combined frequency spectrum data is provided to theanalyzer 103. In some embodiments, the different spectrum data isprovided to analyzer 103 separately, and then combined by analyzer 103.Before and after data capture, the tuner may be utilized by the user totune to video or data services.

Once an anomaly is detected and located, FIG. 5 includes steps 560 and570 for determining the impact of the anomaly on services (e.g., videoand data services) provided over the network. FIG. 18 illustrates aprocess 1800 for performing these steps in more detail. In step 1810, aspectrum allocation of services (e.g., video, audio, DOCSIS, MOCA, etc.)to channels is retrieved from a database. In step 1820, a SNR is derivedfor one or more channels based on the detected anomaly and frequencyspectrum data. For example, if a standing wave is detected, the channelswhere the standing wave minimums are located may be determined, and theSNR of those channels may be calculated based on the frequency spectrumdata in those channel bandwidths. In step 1830, the spectrum allocationdatabase is compared to the calculated SNRs to identify those channelsthat may potentially be impacted by a reduced SNR. In step 1840, thecalculated SNRs of the potentially impacted channels are compared topredetermined threshold SNRs that may indicate a SNR level below whichresults in a degraded service (e.g., pixelated video). The threshold SNRmay be different for different types of service. For example, a DOCSISdata service may be impacted to a greater extent than video for the sameSNR. Channels having an SNR below their respective thresholds for thetype of service they carry are identified as impacted channels. In step1850, a course of action is determined for the impacted channels. Forexample, if a standing wave was detected and located within a customer'spremises, but no channels in the customer's premises were impacted,analyzer 103 may determine that no course of action should be taken. Asanother example, the customer may have a channel that is degraded, butit may be a service to which the customer does not subscribe. In such acase, the analyzer 103 may still determine not to take action. Inanother example, if the customer's subscribed services were impacted,analyzer 103 may provide the location of the fault causing the standingwave to a technician or to the customer with suggested directions ofcorrecting the fault (e.g., tighten connection at drop line to premises,remove splitter, remove filter, etc.).

FIG. 19 is a block diagram of an illustrative analyzer 103 according tosome embodiments. In at least some embodiments, analyzer 103 can beimplemented as (or as part of) a server or other computer platform. Sucha computer platform could be dedicated to performing analyzer 103operations described herein, or could additionally perform otheroperations. Analyzer 103 may communicate with hub 102 and/or othernetwork elements over one or more network interfaces (i/f) 1903.Interface 1903 could be, e.g., a Gigabit Ethernet card, 802.11 wirelessinterface, etc. Analyzer 103 may further include memory 1902 for storingmachine-readable instructions and data and a processor 1901 forexecuting the instructions and controlling operations of analyzer 103 toperform the various functions described herein. Although a single blockis shown for memory 1902 and a single block is shown for processor 1901,data/instruction storage and computational operations of analyzer 103could respectively be distributed across multiple memory devices andmultiple processors located within analyzer 103 or spread acrossmultiple platforms (e.g., multiple computers, servers, mainframes,etc.). Memory 1902 may include volatile and non-volatile memory and caninclude any of various types of storage technology, including but notlimited to read only memory (ROM) modules, random access memory (RAM)modules, magnetic tape, magnetic discs (e.g., a fixed hard disk drive ora removable floppy disk), optical disk (e.g., a CD-ROM disc, a CD-RWdisc, a DVD disc), flash memory, and EEPROM memory, or other deviceswith equivalent capabilities. Processor 1901 may be implemented with anyof numerous types of devices, including but not limited to one or moremicroprocessors, microcontrollers, digital signal processors, embeddedprocessors, application specific integrated circuits, field programmablegate arrays, and combinations thereof. In at least some embodiments,processor 1901 carries out operations of analyzer 103 described hereinaccording to machine-readable instructions (e.g., software) stored inmemory 1902 and/or stored as hardwired logic gates within processor1901. Processor 1901 may communicate with and control memory 1902 andinterface 1903 over one or more buses 1904.

Analyzer 103 may output data to a display 1906 using video interface(i/f) 1905. Although not shown, analyzer 103 may also receive user inputvia a keyboard, mouse, finger or other user input device. In someembodiments, analyzer 103 may communicate with other computers anddevices over network interface 1903. For example, a user having a remotecomputer (e.g., a laptop computer, PDA, smartphone, etc.) couldestablish a communication session with analyzer 103 over one or morenetwork links. The user could provide instructions, submit queries, orotherwise interact with analyzer 103 by sending communications over thenetwork links via the remote computer. Analyzer 103 could then providedata outputs to the user's remote computer over those same or otherlinks, which data could then be output on a display of the user'scomputer (e.g., a web server).

The foregoing description of embodiments has been presented for purposesof illustration and description. The foregoing description is notintended to be exhaustive or to limit embodiments to the precise formdisclosed, and modifications and variations are possible in light of theabove teachings or may be acquired from practice of various embodiments.The embodiments discussed herein were chosen and described in order toexplain the principles and the nature of various embodiments and theirpractical application to enable one skilled in the art to utilize thepresent invention in various embodiments and with various modificationsas are suited to the particular use contemplated. All embodiments neednot necessarily achieve all objects or advantages identified above. Anyand all permutations of various features described herein are within thescope of the invention. For example, while in FIGS. 3A-3B, 4A-4L, and 15the horizontal axis is in MHz and the vertical axis is in dBmV, othervariations may use other scales for displaying the data. As anotherexample, all steps in the processes of FIGS. 5, 8A-8B, and 11-14 may notbe performed, and the steps may be performed in a different order thanhow is illustrated and described. For example, in some embodiments, oneor more of steps 810-840, 1110-1120, 1210-1220, 1310-1320, and 1410-1420may be performed together resulting in the detection of one or more ofthe faults illustrated in FIGS. 4A-4L. The decision block 845, 1125,1225, 1325, and 1425 may then be combined (e.g., as in step 540) tocompare the detected faults to a library of known pre-characterizedfault types to select which of the analyses of FIGS. 8, 11, 12, 13, and14 to continue to determine the location of the fault(s).

The invention claimed is:
 1. A method comprising: accessing frequencycharacterization data of a plurality of devices in a network; based onthe frequency characterization data of the plurality of devices:identifying a first subset of the plurality of devices in the networkthat exhibit a common frequency peak or common frequency attenuation;identifying a second subset of the plurality of devices in the networkthat do not exhibit the common frequency peak or the common frequencyattenuation; identifying a direction of signals of the first subset ofthe plurality of devices in relation to the second subset of theplurality of devices; identifying a location of least one malfunctioningdevice based on the direction of signals of the first subset of theplurality of devices in relation to the second subset of the pluralityof devices; and outputting the location of the at least onemalfunctioning device.
 2. The method of claim 1, further comprising:comparing a first subset of frequency characterization data associatedwith the first subset of the plurality of devices to a second subset offrequency characterization data associated with the second subset of theplurality of devices.
 3. The method of claim 2, further comprising:identifying a first frequency peak or first frequency attenuation in thefirst subset of frequency characterization data; identifying a secondfrequency peak or second frequency attenuation in the second subset offrequency characterization data; and comparing at least one of a centerfrequency, bandwidth, or fitted curve of the first frequency peak orfirst frequency attenuation to at least one of a center frequency,bandwidth, or fitted curve of the second frequency peak or the secondfrequency attenuation.
 4. The method of claim 3, wherein the first peakis identified by detecting a frequency band that exceeds a predeterminedamplitude for a predetermined bandwidth.
 5. The method of claim 3,wherein the first frequency attenuation is identified by detecting afrequency band that is attenuated to a predetermined amplitude for apredetermined bandwidth.
 6. The method of claim 1, wherein identifying adirection of signals of the first subset of the plurality of devices inrelation to the second subset of the plurality of devices, comprises:identifying a first direction of signals in a first bandwidth where thefirst frequency peak or the first frequency attenuation is located; andidentifying a second direction of signals in a second bandwidth wherethe second frequency peak or the second frequency attenuation islocated, wherein the first direction of signals is different from thesecond direction of signals.
 7. The method of claim 1, wherein theplurality of devices is configured to transmit at least one of upstreamcommunication or downstream communication based on the direction ofsignals.
 8. The method of claim 1, wherein the at least onemalfunctioning device exhibits a temperature dependent fault.
 9. Themethod of claim 1, wherein the at least one malfunctioning device is amalfunctioning amplifier that includes anomaly.
 10. The method of claim1, further comprising: generating for display a geospatial map of theplurality of devices on the network.
 11. A system comprising: controlcircuitry configured to: access frequency characterization data of aplurality of devices in a network; based on the frequencycharacterization data of the plurality of devices: identify a firstsubset of the plurality of devices in the network that exhibit a commonfrequency peak or common frequency attenuation; identify a second subsetof the plurality of devices in the network that do not exhibit thecommon frequency peak or the common frequency attenuation; identify adirection of signals of the first subset of the plurality of devices inrelation to the second subset of the plurality of devices on thenetwork; identify a location of least one malfunctioning device based onthe direction of signals of the first subset of the plurality of devicesin relation to the second subset of the plurality of devices; andinput/output circuitry configured to: output the location of the atleast one malfunctioning device.
 12. The system of claim 11, wherein thecontrol circuitry is further configured to: compare a first subset offrequency characterization data associated with the first subset of theplurality of devices to a second subset of frequency characterizationdata associated with the second subset of the plurality of devices. 13.The system of claim 12, wherein the control circuitry is furtherconfigured to: identify a first frequency peak or first frequencyattenuation in the first subset of frequency characterization data;identify a second frequency peak or second frequency attenuation in thesecond subset of frequency characterization data; and compare at leastone of a center frequency, bandwidth, or fitted curve of the firstfrequency peak or first frequency attenuation to at least one of acenter frequency, bandwidth, or fitted curve of the second frequencypeak or the second frequency attenuation.
 14. The system of claim 13,wherein the first peak is identified by detecting a frequency band thatexceeds a predetermined amplitude for a predetermined bandwidth.
 15. Thesystem of claim 13, wherein the first frequency attenuation isidentified by detecting a frequency band that is attenuated to apredetermined amplitude for a predetermined bandwidth.
 16. The system ofclaim 11, wherein the control circuitry, when identifying a direction ofsignals of the first subset of the plurality of devices in relation tothe second subset of the plurality of devices, is further configured to:identify a first direction of signals in a first bandwidth where thefirst frequency peak or the first frequency attenuation is located; andidentify a second direction of signals in a second bandwidth where thesecond frequency peak or the second frequency attenuation is located,wherein the first direction of signals is different from the seconddirection of signals.
 17. The system of claim 11, wherein the pluralityof devices is configured to transmit at least one of upstreamcommunication or downstream communication based on the direction ofsignals.
 18. The system of claim 11, wherein the at least onemalfunctioning device exhibits a temperature dependent fault.
 19. Thesystem of claim 11, wherein the at least one malfunctioning device is amalfunctioning amplifier that includes anomaly.
 20. The system of claim11, wherein the control circuitry is further configured to: generate fordisplay a geospatial map of the plurality of devices on the network.